Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.

Objectives: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected infection). Materials and Methods: This multicenter retrospective study included admissions at 2 medical centers that spanned 2007-2022. Distinct datasets were created for each clinical task, with 1 site used for training and the other for testing. Three feature engineering methods (normalization, standardization, and piece-wise linear encoding with decision trees [PLE-DTs]) and 3 architectures (long short-term memory/gated recurrent unit [LSTM/GRU], temporal convolutional network, and time-distributed wrapper with convolutional neural network [TDW-CNN]) were compared in each clinical task. Model discrimination was evaluated using the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). Results: The study comprised 373 825 admissions for training and 256 128 admissions for testing. LSTM/GRU models tied with TDW-CNN models with both obtaining the highest mean AUPRC in 2 tasks, and LSTM/GRU had the highest mean AUROC across all tasks (deterioration: 0.81, AKI: 0.92, infection: 0.87). PLE-DT with LSTM/GRU achieved the highest AUPRC in all tasks. Discussion: When externally validated in 3 clinical tasks, the LSTM/GRU model architecture with PLE-DT transformed data demonstrated the highest AUPRC in all tasks. Multiple models achieved similar performance when evaluated using AUROC. Conclusion: The LSTM architecture performs as well or better than some newer architectures, and PLE-DT may enhance the AUPRC in variable-length time series data for predicting clinical outcomes during external validation.

Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients. This multicenter retrospective study of admissions to 6 hospitals included “gold-standard” labels of infection from manual chart review and “silver-standard” labels from nonchart-reviewed patients using the Sepsis-3 infection criteria based on antibiotic and culture orders. “Gold-standard” labeled admissions were randomly allocated to training (70%) and testing (30%) datasets. Using patient characteristics, vital signs, and laboratory data from the first 24 hours of admission, we derived deep learning and non-deep learning models using transfer learning and semi-supervised methods. Performance was compared in the gold-standard test set using discrimination and calibration metrics. The study comprised 432 965 admissions, of which 2724 underwent chart review. In the test set, deep learning and non-deep learning approaches had similar discrimination (area under the receiver operating characteristic curve of 0.82). Semi-supervised and transfer learning approaches did not improve discrimination over models fit using only silver- or gold-standard data. Transfer learning had the best calibration (unreliability index P value: .997, Brier score: 0.173), followed by self-learning gradient boosted machine (P value: .67, Brier score: 0.170). Deep learning and non-deep learning models performed similarly for identifying infection, as did models developed using Sepsis-3 and manual chart review labels. In a multicenter study of almost 3000 chart-reviewed patients, semi-supervised and transfer learning models showed similar performance for model discrimination as baseline XGBoost, while transfer learning improved calibration.

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Blindness or vision impairment, one of the top ten disabilities among men and women, targets more than 7 million Americans of almost all ages. Accessible visual information is of paramount importance to improve independence and safety of blind and visually-impaired people, and there is a pressing need to develop smart automated systems to assist their navigation, particularly in unfamiliar healthcare environments, such as clinics, hospitals, and urgent cares. Objective: The main objective of the current contribution was to develop computational vision algorithms composed with deep neural network to assist visually impaired individual's mobility in clinical environments by accurately detecting doors, stairs, and signages, the most remarkable landmarks. Methods: A dataset of indoor landmarks of interests was collected from Marshfield Clinic in Marshfield. To increase the accuracy and stability of predictive model, an augmented dataset was generated by adding several variations to the collected one. A deep convolutional neural network (CNN), namely “AlexNet” was then utilized and trained with both datasets. The CNN model was configured in two different strategies: (1) Transfer Learning (TL), (2) Feature Extraction (FEx) combined with three different classifiers, namely Support Vector Machine (SVM), Naïve Bayesian (NB) and K-Nearest Neighbor (KNN). To speed up the training process and make a real-time decision, NVIDIA GPU Quadro M5000 was employed. Results: The CNN is trained with TL and FEx approaches using 10%, 20% and 30% of both collected and augmented datasets. While training time of FEx model is in the order of a few minutes, TL model requires 10-20 times more time. The average object recognition time of both approaches is less than 0.3 seconds. The accuracy of recognizing objects of interest is more than 98% regardless of training size. However, the accuracy of TL model varies from about 60% to 99% with respect to the training size. Conclusions: Even though GPU processors extremely increase the speed of computing compared with CPUs, training time of TL approach is unbearable. However, both approaches on the stage of test sample evaluation are fast enough to be used in real-time applications. FEx accompanied with either NB, SVM or KNN classifiers achieves accuracy of greater than 98% even with small training set size. To compete with FEx approach with respect to accuracy, recall and precision, TL requires larger training set. For detecting three objects of interest that are common in any health care facility, the CNN model based on FEx is a pragmatic option. The proposed framework has the potential to recognize a variety of objects in the indoor environment, and ultimately to help visually-impaired people navigate their way in the clinic independently.