Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional. In this paper, we propose a generative consistency for semi-supervised (GCS) model. Considering the rich information contained in the feature map, the GCS model utilizes the generation results to constrain the segmentation model. The generated data comes from labeled data, unlabeled data, and unlabeled data after perturbation, respectively. The GCS model also calculates the consistency of the perturbed data to improve the feature mining ability. Subsequently, we propose a new model as the backbone of the GSC model. It transfers TOF-MRA into graph space and establishes correlation using Transformer. We demonstrated the effectiveness of the proposed model on TOF-MRA representations, and tested the GCS model with state-of-the-art semi-supervised methods using the proposed model as backbone. The experiments prove the important role of the GCS model in cerebrovascular segmentation. Code is available at https://github.com/MontaEllis/SSL-For-Medical-Segmentation.

Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification, and explainability. The re-parameterization method is applied to our classification network, and the effect of explainable heatmaps is improved by modifying the network architecture. Our classification model also has the advantage of post-hoc explainability. We used the BraTS-2018 dataset for training and verification. Experimental results show that our simplified framework has excellent performance and high calculation speed. The comparison of results by segmentation and explainable neural networks helps researchers better understand the process of the black box method, increase the trust of the deep model output, and make more accurate judgments in disease identification and diagnosis.

Retinal blood vessels are the basis for clinical diagnosis of some diseases. Achieving automatic retinal vessel segmentation from fundus images is an important and challenging work. In this paper, a neural network architecture based on the Dense U-net using Inception module is proposed for retinal vessel segmentation. First, the skip connections in traditional U-net are replaced with Dense Block to achieve full fusion of features from shallow layers to deep layers. Then, the Inception module is applied to supersede the traditional convolution operation. Thus, vessel features corresponding to the convolution kernels of different sizes can be extracted. Finally, Generative Adversarial Networks (GAN) are adopted in the training phase. The Dense U-net using Inception module is treated as the generator of GAN, and a multilayer neural network is created as the discriminator of GAN. The generator and discriminator are trained alternately. The loss function is a combination of segmentation loss and GAN loss. So that segmentation results can be fitted to the ground truth from both pixel value and pixel distribution. The algorithm proposed in this paper is verified on the public Digital Retinal Images for Vessel Extraction (DRIVE) dataset, where the Dice rate reaches 82.15%, and the AU-ROC and AU-PR reach 0.9772 and 0.9058, respectively. Experiments show that the proposed algorithm is effective in realizing automatic retinal vessel segmentation.

The human attention mechanism can be understood and simulated by closely associating the saliency prediction task to neuroscience and psychology. Furthermore, saliency prediction is widely used in computer vision and interdisciplinary subjects. In recent years, with the rapid development of deep learning, deep models have made amazing achievements in saliency prediction. Deep learning models can automatically learn features, thus solving many drawbacks of the classic models, such as handcrafted features and task settings, among others. Nevertheless, the deep models still have some limitations, for example in tasks involving multi-modality and semantic understanding. This study focuses on summarizing the relevant achievements in the field of saliency prediction, including the early neurological and psychological mechanisms and the guiding role of classic models, followed by the development process and data comparison of classic and deep saliency prediction models. This study also discusses the relationship between the model and human vision, as well as the factors that cause the semantic gaps, the influences of attention in cognitive research, the limitations of the saliency model, and the emerging applications, to provide new saliency predictions for follow-up work and the necessary help and advice.