Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI

Multi-residual 2D network integrating spatial correlation for whole heart segmentation

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder-decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://github.com/nancy1984yan/MultiResNet-SC.

The application of artificial intelligence in EUS

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT's limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.

CASMatching strategy for automated detection and quantification of carotid artery stenosis based on digital subtraction angiography

BACKGROUND AND OBJECTIVE

Automated detection and quantification of carotid artery stenosis is a crucial task in establishing a computer-aided diagnostic system for brain diseases. Digital subtraction angiography (DSA) is known as the "gold standard" for carotid stenosis diagnosis. It is commonly used to identify carotid artery stenosis and measure morphological indices of the stenosis. However, using deep learning to detect stenosis based on DSA images and further quantitatively predicting the morphological indices remain a challenge due the absence of prior work. In this paper, we propose a quantitative method for predicting morphological indices of carotid stenosis.

METHODS

Our method adopts a two-stage pipeline, first locating regions suitable for predicting morphological indices by object detection model, and then using a regression model to predict indices. A novel Carotid Artery Stenosis Matching (CASMatching) strategy is introduced into the object detection to model the matching relationship between a stenosis and multiple normal vessel segments. The proposed Match-ness branch predicts a Match-ness score for each normal vessel segment to indicate the degree of matching to the stenosis. A novel Direction Distance-IoU (2DIoU) loss based on the Distance-IoU loss is proposed to make the model focused more on the bounding box regression in the direction of vessel extension. After detection, the normal vessel segment with the highest Match-ness score and the stenosis are intercepted from the original image, then fed into a regression model to predict morphological indices and calculate the degree of stenosis.

RESULTS

Our method is trained and evaluated on a dataset collected from three different manufacturers' monoplane X-ray systems. The results show that the proposed components in the object detector substantially improve the detection performance of normal vascular segments. For the prediction of morphological indices, our model achieves Mean Absolute Error of 0.378, 0.221, 4.9 on reference vessel diameter (RVD), minimum lumen diameter (MLD) and stenosis degree.

CONCLUSIONS

Our method can precisely localize the carotid stenosis and the normal vessel segment suitable for predicting RVD of the stenosis, and further achieve accurate quantification, providing a novel solution for the quantification of carotid artery stenosis.

End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography

BACKGROUND

Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes.

PURPOSE

In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke.

METHODS

We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices.

RESULTS

Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization.

CONCLUSION

Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings.