Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniques

Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usage. However, segmenting cardio-cerebrovascular structures from medical images is very challenging due to the presence of thin or blurred vascular shapes, imbalanced distribution of vessel and non-vessel pixels, and interference from imaging artifacts. These difficulties make manual or semi-manual segmentation methods highly time-consuming, labor-intensive, and prone to errors with interobserver variability, where different experts may produce different segmentations from a variety of modalities. Consequently, there is a growing interest in developing automated algorithms. This paper provides an up-to-date survey of deep learning techniques, for cardio-cerebrovascular segmentation. It analyzes the research landscape, surveys recent approaches, and discusses challenges such as the scarcity of accurately annotated data and variability. This paper also illustrates the urgent needs for developing multi-modality label-efficient deep learning techniques. To the best of our knowledge, this paper is the first comprehensive survey of deep learning approaches that effectively segment vessels in both the heart and brain. It aims to advance automated segmentation techniques for cardio-cerebrovascular diseases, benefiting researchers and healthcare professionals.

Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.