Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain pathology.However,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural priority.To overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline modeling.The proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context fusion.To exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical directions.For maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby coordinates.Extensive experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

Advances in Deep Learning-Based Medical Image Analysis

Importance.With the booming growth of artificial intelligence(AI),especially the recent advancements of deep learning,utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia.This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications.It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights.This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications.More specifically,state-ofthe-art clinical applications include four major human body systems:the nervous system,the cardiovascular system,the digestive system,and the skeletal system.Overall,according to the best available evidence,deep learning models performed well in medical image analysis,but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability.Future direction could include federated learning,benchmark dataset collection,and utilizing domain subject knowledge as priors.Conclusion.Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy,efficiency,stability,and scalability.Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.