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Corn cob is considered an agricultural bio-waste that can be reused and incorporated in the building industry as a thermal insulation material. However, more research is required to obtain a more detailed analysis in what concerns building materials' thermal performance using this waste as raw material and, consequently, their sustainability profile. In this context, this study aims to evaluate the thermal behaviour and the environmental impact of two different corn cob particleboards using two types of glue binders: Polyvinyl Acetate (PVA) and Fabricol AG222 (FAG222). An experimental study was performed to analyse the particleboards’ thermal performance, allowing the estimation of the thermal transmission coefficient. A sustainability analysis was carried out using a lifecycle assessment (LCA) tool according to ISO 14040 and ISO 14044. The functional unit “mass of material required to provide a thermal resistance (R) of 1 m2°C/W″ was considered for the calculation of the environmental impacts. The production and disposal phases (incineration and landfill) were considered. The results revealed that both corn cob particleboards have potential to be used as a sustainable building material for the thermal insulation of walls, considering the values obtained for the thermal performance parameters. Average values of 1.33 W/m2°C and 0.052 W/m°C were achieved for the thermal transmission coefficient and thermal conductivity of the PVA particleboard. For FAG222, those values corresponded to 1.92 W/m2°C and 0.087 W/m°C, showing similarities with the current insulation materials. Both options display environmentally friendly profiles, although the particleboard with PVA offers enhanced results when landfill is the preferred disposal method. This research work is thus a contribution to the scientific knowledge regarding the valorisation of agriculture wastes and by-products as potential eco-friendly building materials. Furthermore, applying this bio-waste as insulation material reveals a consistent path on circular economy.
Ana Ramos; Ana Briga-Sá; Sandra Pereira; Mariana Correia; Jorge Pinto; Isabel Bentes; Carlos A. Teixeira. Thermal performance and life cycle assessment of corn cob particleboards. Journal of Building Engineering 2021, 44, 102998 .
AMA StyleAna Ramos, Ana Briga-Sá, Sandra Pereira, Mariana Correia, Jorge Pinto, Isabel Bentes, Carlos A. Teixeira. Thermal performance and life cycle assessment of corn cob particleboards. Journal of Building Engineering. 2021; 44 ():102998.
Chicago/Turabian StyleAna Ramos; Ana Briga-Sá; Sandra Pereira; Mariana Correia; Jorge Pinto; Isabel Bentes; Carlos A. Teixeira. 2021. "Thermal performance and life cycle assessment of corn cob particleboards." Journal of Building Engineering 44, no. : 102998.
Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.
Tania Pereira; Joana Morgado; Francisco Silva; Michele Pelter; Vasco Dias; Rita Barros; Cláudia Freitas; Eduardo Negrão; Beatriz Flor de Lima; Miguel Correia da Silva; António Madureira; Isabel Ramos; Venceslau Hespanhol; José Costa; António Cunha; Hélder Oliveira. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare 2021, 9, 827 .
AMA StyleTania Pereira, Joana Morgado, Francisco Silva, Michele Pelter, Vasco Dias, Rita Barros, Cláudia Freitas, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António Madureira, Isabel Ramos, Venceslau Hespanhol, José Costa, António Cunha, Hélder Oliveira. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare. 2021; 9 (7):827.
Chicago/Turabian StyleTania Pereira; Joana Morgado; Francisco Silva; Michele Pelter; Vasco Dias; Rita Barros; Cláudia Freitas; Eduardo Negrão; Beatriz Flor de Lima; Miguel Correia da Silva; António Madureira; Isabel Ramos; Venceslau Hespanhol; José Costa; António Cunha; Hélder Oliveira. 2021. "Sharing Biomedical Data: Strengthening AI Development in Healthcare." Healthcare 9, no. 7: 827.
Lung cancer is the type of cancer with highest mortality worldwide. Low-dose computerized tomography is the main tool used for lung cancer screening in clinical practice, allowing the visualization of lung nodules and the assessment of their malignancy. However, this evaluation is a complex task and subject to inter-observer variability, which has fueled the need for computer-aided diagnosis systems for lung nodule malignancy classification. While promising results have been obtained with automatic methods, it is often not straightforward to determine which features a given model is basing its decisions on and this lack of explainability can be a significant stumbling block in guaranteeing the adoption of automatic systems in clinical scenarios. Though visual malignancy assessment has a subjective component, radiologists strongly base their decision on nodule features such as nodule spiculation and texture, and a malignancy classification model should thus follow the same rationale. As such, this study focuses on the characterization of lung nodules as a means for the classification of nodules in terms of malignancy. For this purpose, different model architectures for nodule characterization are proposed and compared, with the final goal of malignancy classification. It is shown that models that combine direct malignancy prediction with specific branches for nodule characterization have a better performance than the remaining models, achieving an Area Under the Curve of 0.783. The most relevant features for malignancy classification according to the model were lobulation, spiculation and texture, which is found to be in line with current clinical practice.
Sónia Marques; Filippo Schiavo; Carlos A. Ferreira; João Pedrosa; António Cunha; Aurélio Campilho. A multi-task CNN approach for lung nodule malignancy classification and characterization. Expert Systems with Applications 2021, 184, 115469 .
AMA StyleSónia Marques, Filippo Schiavo, Carlos A. Ferreira, João Pedrosa, António Cunha, Aurélio Campilho. A multi-task CNN approach for lung nodule malignancy classification and characterization. Expert Systems with Applications. 2021; 184 ():115469.
Chicago/Turabian StyleSónia Marques; Filippo Schiavo; Carlos A. Ferreira; João Pedrosa; António Cunha; Aurélio Campilho. 2021. "A multi-task CNN approach for lung nodule malignancy classification and characterization." Expert Systems with Applications 184, no. : 115469.
Liquid biopsy is an emerging technology with a potential role in the screening and early detection of lung cancer. Several liquid biopsy-derived biomarkers have been identified and are currently under ongoing investigation. In this article, we review the available data on the use of circulating biomarkers for the early detection of lung cancer, focusing on the circulating tumor cells, circulating cell-free DNA, circulating micro-RNAs, tumor-derived exosomes, and tumor-educated platelets, providing an overview of future potential applicability in the clinical practice. While several biomarkers have shown exciting results, diagnostic performance and clinical applicability is still limited. The combination of different biomarkers, as well as their combination with other diagnostic tools show great promise, although further research is still required to define and validate the role of liquid biopsies in clinical practice.
Cláudia Freitas; Catarina Sousa; Francisco Machado; Mariana Serino; Vanessa Santos; Natália Cruz-Martins; Armando Teixeira; António Cunha; Tania Pereira; Hélder P. Oliveira; José Luís Costa; Venceslau Hespanhol. The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer. Frontiers in Oncology 2021, 11, 1 .
AMA StyleCláudia Freitas, Catarina Sousa, Francisco Machado, Mariana Serino, Vanessa Santos, Natália Cruz-Martins, Armando Teixeira, António Cunha, Tania Pereira, Hélder P. Oliveira, José Luís Costa, Venceslau Hespanhol. The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer. Frontiers in Oncology. 2021; 11 ():1.
Chicago/Turabian StyleCláudia Freitas; Catarina Sousa; Francisco Machado; Mariana Serino; Vanessa Santos; Natália Cruz-Martins; Armando Teixeira; António Cunha; Tania Pereira; Hélder P. Oliveira; José Luís Costa; Venceslau Hespanhol. 2021. "The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer." Frontiers in Oncology 11, no. : 1.
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.
Joana Morgado; Tania Pereira; Francisco Silva; Cláudia Freitas; Eduardo Negrão; Beatriz de Lima; Miguel da Silva; António Madureira; Isabel Ramos; Venceslau Hespanhol; José Costa; António Cunha; Hélder Oliveira. Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer. Applied Sciences 2021, 11, 3273 .
AMA StyleJoana Morgado, Tania Pereira, Francisco Silva, Cláudia Freitas, Eduardo Negrão, Beatriz de Lima, Miguel da Silva, António Madureira, Isabel Ramos, Venceslau Hespanhol, José Costa, António Cunha, Hélder Oliveira. Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer. Applied Sciences. 2021; 11 (7):3273.
Chicago/Turabian StyleJoana Morgado; Tania Pereira; Francisco Silva; Cláudia Freitas; Eduardo Negrão; Beatriz de Lima; Miguel da Silva; António Madureira; Isabel Ramos; Venceslau Hespanhol; José Costa; António Cunha; Hélder Oliveira. 2021. "Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer." Applied Sciences 11, no. 7: 3273.
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen’s kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen’s kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions - data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision-making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient follow-up recommendation.
João Pedrosa; Guilherme Aresta; Carlos Ferreira; GurRaj Atwal; Hady Ahmady Phoulady; Xiaoyu Chen; Rongzhen Chen; Jiaoliang Li; Liansheng Wang; Adrian Galdran; Hamid Bouchachia; Krishna Chaitanya Kaluva; Kiran Vaidhya; Abhijith Chunduru; Sambit Tarai; Sai Prasad Pranav Nadimpalli; Suthirth Vaidya; Ildoo Kim; Alexandr Rassadin; Zhenhuan Tian; Zhongwei Sun; Yizhuan Jia; Xuejun Men; Isabel Ramos; António Cunha; Aurélio Campilho. LNDb challenge on automatic lung cancer patient management. Medical Image Analysis 2021, 70, 102027 .
AMA StyleJoão Pedrosa, Guilherme Aresta, Carlos Ferreira, GurRaj Atwal, Hady Ahmady Phoulady, Xiaoyu Chen, Rongzhen Chen, Jiaoliang Li, Liansheng Wang, Adrian Galdran, Hamid Bouchachia, Krishna Chaitanya Kaluva, Kiran Vaidhya, Abhijith Chunduru, Sambit Tarai, Sai Prasad Pranav Nadimpalli, Suthirth Vaidya, Ildoo Kim, Alexandr Rassadin, Zhenhuan Tian, Zhongwei Sun, Yizhuan Jia, Xuejun Men, Isabel Ramos, António Cunha, Aurélio Campilho. LNDb challenge on automatic lung cancer patient management. Medical Image Analysis. 2021; 70 ():102027.
Chicago/Turabian StyleJoão Pedrosa; Guilherme Aresta; Carlos Ferreira; GurRaj Atwal; Hady Ahmady Phoulady; Xiaoyu Chen; Rongzhen Chen; Jiaoliang Li; Liansheng Wang; Adrian Galdran; Hamid Bouchachia; Krishna Chaitanya Kaluva; Kiran Vaidhya; Abhijith Chunduru; Sambit Tarai; Sai Prasad Pranav Nadimpalli; Suthirth Vaidya; Ildoo Kim; Alexandr Rassadin; Zhenhuan Tian; Zhongwei Sun; Yizhuan Jia; Xuejun Men; Isabel Ramos; António Cunha; Aurélio Campilho. 2021. "LNDb challenge on automatic lung cancer patient management." Medical Image Analysis 70, no. : 102027.
Multi-temporal InSAR (MT-InSAR) techniques proved to be very effective for deformation monitoring. However, decorrelation and other noise sources, can be limiting factors in MT-InSAR. The obtained observations (PS - Persistent scatterers) are usually very demanding from a computational perspective, as they can reach hundreds of thousands of observations. To simplify and speed up the classification process, in this study we present an approach based on Convolutional Neural Networks (CNN) classification models, for the detection of MT-InSAR outlying observations. For each PS, the corresponding MT-InSAR parameters, its neighbouring scatterers parameters and its relative position are considered. Tests in two independent PS datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models are robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained.
Pedro Aguiar; António Cunha; Matus Bakon; Antonio M. Ruiz-Armenteros; Joaquim J. Sousa. Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning. Procedia Computer Science 2021, 181, 1146 -1153.
AMA StylePedro Aguiar, António Cunha, Matus Bakon, Antonio M. Ruiz-Armenteros, Joaquim J. Sousa. Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning. Procedia Computer Science. 2021; 181 ():1146-1153.
Chicago/Turabian StylePedro Aguiar; António Cunha; Matus Bakon; Antonio M. Ruiz-Armenteros; Joaquim J. Sousa. 2021. "Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning." Procedia Computer Science 181, no. : 1146-1153.
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.
Tania Pereira; Cláudia Freitas; José Luis Costa; Joana Morgado; Francisco Silva; Eduardo Negrão; Beatriz Flor De Lima; Miguel Correia Da Silva; António J. Madureira; Isabel Ramos; Venceslau Hespanhol; António Cunha; Hélder P. Oliveira. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. Journal of Clinical Medicine 2020, 10, 118 .
AMA StyleTania Pereira, Cláudia Freitas, José Luis Costa, Joana Morgado, Francisco Silva, Eduardo Negrão, Beatriz Flor De Lima, Miguel Correia Da Silva, António J. Madureira, Isabel Ramos, Venceslau Hespanhol, António Cunha, Hélder P. Oliveira. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. Journal of Clinical Medicine. 2020; 10 (1):118.
Chicago/Turabian StyleTania Pereira; Cláudia Freitas; José Luis Costa; Joana Morgado; Francisco Silva; Eduardo Negrão; Beatriz Flor De Lima; Miguel Correia Da Silva; António J. Madureira; Isabel Ramos; Venceslau Hespanhol; António Cunha; Hélder P. Oliveira. 2020. "Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images." Journal of Clinical Medicine 10, no. 1: 118.
Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Computed Tomography (CT) scans to assess the nodules and the likelihood of malignancy. Automatic solutions can help to make a faster and more accurate diagnosis, which is crucial for the early detection of lung cancer. Convolutional neural networks (CNN) based approaches have shown to provide a reliable feature extraction ability to detect the malignancy risk associated with pulmonary nodules. This type of approach requires a massive amount of data to model training, which usually represents a limitation in the biomedical field due to medical data privacy and security issues. Transfer learning (TL) methods have been widely explored in medical imaging applications, offering a solution to overcome problems related to the lack of training data publicly available. For the clinical annotations experts with a deep understanding of the complex physiological phenomena represented in the data are required, which represents a huge investment. In this direction, this work explored a TL method based on unsupervised learning achieved when training a Convolutional Autoencoder (CAE) using images in the same domain. For this, lung nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) were extracted and used to train a CAE. Then, the encoder part was transferred, and the malignancy risk was assessed in a binary classification—benign and malignant lung nodules, achieving an Area Under the Curve (AUC) value of 0.936. To evaluate the reliability of this TL approach, the same architecture was trained from scratch and achieved an AUC value of 0.928. The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.
Francisco Silva; Tania Pereira; Julieta Frade; José Mendes; Claudia Freitas; Venceslau Hespanhol; José Luis Costa; António Cunha; Hélder P. Oliveira. Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images. Applied Sciences 2020, 10, 7837 .
AMA StyleFrancisco Silva, Tania Pereira, Julieta Frade, José Mendes, Claudia Freitas, Venceslau Hespanhol, José Luis Costa, António Cunha, Hélder P. Oliveira. Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images. Applied Sciences. 2020; 10 (21):7837.
Chicago/Turabian StyleFrancisco Silva; Tania Pereira; Julieta Frade; José Mendes; Claudia Freitas; Venceslau Hespanhol; José Luis Costa; António Cunha; Hélder P. Oliveira. 2020. "Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images." Applied Sciences 10, no. 21: 7837.
Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547.
Hasan Zengin; José Camara; Paulo Coelho; João M. F. Rodrigues; António Cunha. Low-Resolution Retinal Image Vessel Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 611 -627.
AMA StyleHasan Zengin, José Camara, Paulo Coelho, João M. F. Rodrigues, António Cunha. Low-Resolution Retinal Image Vessel Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():611-627.
Chicago/Turabian StyleHasan Zengin; José Camara; Paulo Coelho; João M. F. Rodrigues; António Cunha. 2020. "Low-Resolution Retinal Image Vessel Segmentation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 611-627.
Currently, lung cancer is the most lethal in the world. In order to make screening and follow-up a little more systematic, guidelines have been proposed. Therefore, this study aimed to create a diagnostic support approach by providing a patient label based on the LUNG-RADS™ guidelines. The only input required by the system is the nodule centroid to take the region of interest for the input of the classification system. With this in mind, two deep learning networks were evaluated: a Wide Residual Network and a DenseNet. Taking into account the annotation uncertainty we proposed to use sample weights that are introduced in the loss function, allowing nodules with a high agreement in the annotation process to take a greater impact on the training error than its counterpart. The best result was achieved with the Wide Residual Network with sample weights achieving a nodule-wise LUNG-RADS™ labelling accuracy of $0.735\pm 0.003$ .
Carlos Alexandre Ferreira; Guilherme Aresta; Joao Pedrosa; Joao Rebelo; Eduardo Negrao; António Cunha; Isabel Ramos; Aurélio Campilho. Classification of Lung Nodules in CT Volumes Using the Lung-RADS™ Guidelines with Uncertainty Parameterization. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020, 791 -794.
AMA StyleCarlos Alexandre Ferreira, Guilherme Aresta, Joao Pedrosa, Joao Rebelo, Eduardo Negrao, António Cunha, Isabel Ramos, Aurélio Campilho. Classification of Lung Nodules in CT Volumes Using the Lung-RADS™ Guidelines with Uncertainty Parameterization. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020; ():791-794.
Chicago/Turabian StyleCarlos Alexandre Ferreira; Guilherme Aresta; Joao Pedrosa; Joao Rebelo; Eduardo Negrao; António Cunha; Isabel Ramos; Aurélio Campilho. 2020. "Classification of Lung Nodules in CT Volumes Using the Lung-RADS™ Guidelines with Uncertainty Parameterization." 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) , no. : 791-794.
Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients’ survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter’s support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task.
Joana Rocha; António Cunha; Ana Maria Mendonça. Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images. Journal of Medical Systems 2020, 44, 1 -8.
AMA StyleJoana Rocha, António Cunha, Ana Maria Mendonça. Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images. Journal of Medical Systems. 2020; 44 (4):1-8.
Chicago/Turabian StyleJoana Rocha; António Cunha; Ana Maria Mendonça. 2020. "Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images." Journal of Medical Systems 44, no. 4: 1-8.
EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures.
Gil Pinheiro; Tania Pereira; Catarina Dias; Cláudia Freitas; Venceslau Hespanhol; Jose Luis Costa; António Cunha; Helder Oliveira. Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS. Scientific Reports 2020, 10, 1 -9.
AMA StyleGil Pinheiro, Tania Pereira, Catarina Dias, Cláudia Freitas, Venceslau Hespanhol, Jose Luis Costa, António Cunha, Helder Oliveira. Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS. Scientific Reports. 2020; 10 (1):1-9.
Chicago/Turabian StyleGil Pinheiro; Tania Pereira; Catarina Dias; Cláudia Freitas; Venceslau Hespanhol; Jose Luis Costa; António Cunha; Helder Oliveira. 2020. "Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS." Scientific Reports 10, no. 1: 1-9.
Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, search lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer aided detection systems can be used as second observers, they may bias radiologists and introduce significant time overheads. With this in mind, this study assesses the potential of using gaze information for integrating automatic detection systems in the clinical practice. For that purpose, 4 radiologists were asked to annotate 20 scans from a public dataset while being monitored by an eye tracker device and an automatic lung nodule detection system was developed. Our results show that radiologists follow a similar search routine and tend to have lower fixation periods in regions where finding errors occur. The overall detection sensitivity of the specialists was 0.67±0.07, whereas the system achieved 0.69. Combining the annotations of one radiologist with the automatic system significantly improves the detection performance to similar levels of two annotators. Likewise, combining the findings of radiologist with the detection algorithm only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false positives. The combination of the automatic system with the gaze information allows to mitigate possible errors of the radiologist without some of the issues usually associated with automatic detection systems.
Guilherme Aresta; Carlos Ferreira; Joao Pedrosa; Teresa Araujo; Joao Rebelo; Eduardo Negrao; Margarida Morgado; Filipe Alves; Antonio Cunha; Isabel Ramos; Aurelio Campilho. Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists’ Screening Performance. IEEE Journal of Biomedical and Health Informatics 2020, 24, 2894 -2901.
AMA StyleGuilherme Aresta, Carlos Ferreira, Joao Pedrosa, Teresa Araujo, Joao Rebelo, Eduardo Negrao, Margarida Morgado, Filipe Alves, Antonio Cunha, Isabel Ramos, Aurelio Campilho. Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists’ Screening Performance. IEEE Journal of Biomedical and Health Informatics. 2020; 24 (10):2894-2901.
Chicago/Turabian StyleGuilherme Aresta; Carlos Ferreira; Joao Pedrosa; Teresa Araujo; Joao Rebelo; Eduardo Negrao; Margarida Morgado; Filipe Alves; Antonio Cunha; Isabel Ramos; Aurelio Campilho. 2020. "Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists’ Screening Performance." IEEE Journal of Biomedical and Health Informatics 24, no. 10: 2894-2901.
The lung cancer diagnosis is based on the search of lung nodules. Besides its characterization, it is also common to register the anatomical position of these findings. Even though computed-aided diagnosis systems tend to help in these tasks, there is still lacking a complete system that can qualitatively label the nodules in lung regions. In this way, this paper proposes an automatic lung reference model to facilitate the report of nodules between computed-aided diagnosis systems and the radiologist, and among radiologists. The model was applied to 115 computed tomography scans with manually and automatically segmented lobes, and the obtained sectors’ variability was evaluated. As the sectors average variability within lobes is less or equal to 0.14, the model can be a good way to promote the report of lung nodules.
Marlene Machado; Carlos Alexandre Ferreira; João Pedrosa; Eduardo Negrão; João Rebelo; Patrícia Leitão; André S. Carvalho; Márcio C. Rodrigues; Isabel Ramos; António Cunha; Aurélio Campilho. Automatic Lung Reference Model. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 2019, 999 -1008.
AMA StyleMarlene Machado, Carlos Alexandre Ferreira, João Pedrosa, Eduardo Negrão, João Rebelo, Patrícia Leitão, André S. Carvalho, Márcio C. Rodrigues, Isabel Ramos, António Cunha, Aurélio Campilho. Automatic Lung Reference Model. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2019; ():999-1008.
Chicago/Turabian StyleMarlene Machado; Carlos Alexandre Ferreira; João Pedrosa; Eduardo Negrão; João Rebelo; Patrícia Leitão; André S. Carvalho; Márcio C. Rodrigues; Isabel Ramos; António Cunha; Aurélio Campilho. 2019. "Automatic Lung Reference Model." VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 , no. : 999-1008.
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening.
João Pedrosa; Guilherme Aresta; João Rebelo; Eduardo Negrão; Isabel Ramos; António Cunha; Aurélio Campilho. LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 2019, 333 -343.
AMA StyleJoão Pedrosa, Guilherme Aresta, João Rebelo, Eduardo Negrão, Isabel Ramos, António Cunha, Aurélio Campilho. LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2019; ():333-343.
Chicago/Turabian StyleJoão Pedrosa; Guilherme Aresta; João Rebelo; Eduardo Negrão; Isabel Ramos; António Cunha; Aurélio Campilho. 2019. "LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening." VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 , no. : 333-343.
This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization.
Joana Rocha; António Cunha; Ana Maria Mendonça. Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 2019, 353 -357.
AMA StyleJoana Rocha, António Cunha, Ana Maria Mendonça. Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2019; ():353-357.
Chicago/Turabian StyleJoana Rocha; António Cunha; Ana Maria Mendonça. 2019. "Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter." VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 , no. : 353-357.
Advances in genomics have driven to the recognition that tumours are populated by different minor subclones of malignant cells that control the way the tumour progresses. However, the spatial and temporal genomic heterogeneity of tumours has been a hurdle in clinical oncology. This is mainly because the standard methodology for genomic analysis is the biopsy, that besides being an invasive technique, it does not capture the entire tumour spatial state in a single exam. Radiographic medical imaging opens new opportunities for genomic analysis by providing full state visualisation of a tumour at a macroscopic level, in a non-invasive way. Having in mind that mutational testing of EGFR and KRAS is a routine in lung cancer treatment, it was studied whether clinical and imaging data are valuable for predicting EGFR and KRAS mutations in a cohort of NSCLC patients. A reliable predictive model was found for EGFR (AUC = 0.96) using both a Multi-layer Perceptron model and a Random Forest model but not for KRAS (AUC = 0.56). A feature importance analysis using Random Forest reported that the presence of emphysema and lung parenchymal features have the highest correlation with EGFR mutation status. This study opens new opportunities for radiogenomics on predicting molecular properties in a more readily available and non-invasive way.
Catarina Dias; Gil Pinheiro; António Cunha; Helder Oliveira. Radiogenomics: Lung Cancer-Related Genes Mutation Status Prediction. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 335 -345.
AMA StyleCatarina Dias, Gil Pinheiro, António Cunha, Helder Oliveira. Radiogenomics: Lung Cancer-Related Genes Mutation Status Prediction. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():335-345.
Chicago/Turabian StyleCatarina Dias; Gil Pinheiro; António Cunha; Helder Oliveira. 2019. "Radiogenomics: Lung Cancer-Related Genes Mutation Status Prediction." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 335-345.
Lung cancer is among the deadliest diseases in the world. The detection and characterization of pulmonary nodules are crucial for an accurate diagnosis, which is of vital importance to increase the patients’ survival rates. The segmentation process contributes to the mentioned characterization, but faces several challenges, due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper proposes two methods for pulmonary nodule segmentation in Computed Tomography (CT) scans. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The second approach is based on Deep Learning, using the U-Net to achieve the same goal. This work compares both performances, and consequently identifies which one is the most promising tool to promote early lung cancer screening and improve nodule characterization. Both methodologies used 2653 nodules from the LIDC database: the SBF based one achieved a Dice score of 0.663, while the U-Net achieved 0.830, yielding more similar results to the ground truth reference annotated by specialists, and thus being a more reliable approach.
Joana Rocha; António Cunha; Ana Maria Mendonça. Comparison of Conventional and Deep Learning Based Methods for Pulmonary Nodule Segmentation in CT Images. Algorithms and Data Structures 2019, 361 -371.
AMA StyleJoana Rocha, António Cunha, Ana Maria Mendonça. Comparison of Conventional and Deep Learning Based Methods for Pulmonary Nodule Segmentation in CT Images. Algorithms and Data Structures. 2019; ():361-371.
Chicago/Turabian StyleJoana Rocha; António Cunha; Ana Maria Mendonça. 2019. "Comparison of Conventional and Deep Learning Based Methods for Pulmonary Nodule Segmentation in CT Images." Algorithms and Data Structures , no. : 361-371.
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule’s boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system’s loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.
Guilherme Aresta; Colin Jacobs; Teresa Araújo; António Cunha; Isabel Ramos; Bram Van Ginneken; Aurélio Campilho. iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Scientific Reports 2019, 9, 1 -9.
AMA StyleGuilherme Aresta, Colin Jacobs, Teresa Araújo, António Cunha, Isabel Ramos, Bram Van Ginneken, Aurélio Campilho. iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Scientific Reports. 2019; 9 (1):1-9.
Chicago/Turabian StyleGuilherme Aresta; Colin Jacobs; Teresa Araújo; António Cunha; Isabel Ramos; Bram Van Ginneken; Aurélio Campilho. 2019. "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network." Scientific Reports 9, no. 1: 1-9.