Curriculum semi-supervised segmentation

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints that tolerate uncertainties with inferred knowledge, e.g., regressed region size, and can be employed for a large variety of region attributes. We evaluated our proposed strategy for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our strategy leverages unlabeled data in more efficiently, and achieves very competitive results, approaching the performance of full-supervision.Comment: Accepted as paper as MICCAI 2O1

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N$^3$ cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N$^3$-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images. Code is available at https://github.com/DeepMed-Lab-ECNU/MagicNet.Comment: Accepted by CVPR 202

MEDICAL MACHINE INTELLIGENCE: DATA-EFFICIENCY AND KNOWLEDGE-AWARENESS

Traditional clinician diagnosis requires massive manual labor from experienced doctors, which is time-consuming and costly. Computer-aided systems are therefore proposed to reduce doctors’ efforts by using machines to automatically make diagnosis and treatment recommendations. The recent success in deep learning has largely advanced the field of computer-aided diagnosis by offering an avenue to deliver automated medical image analysis. Despite such progress, there remain several challenges towards medical machine intelligence, such as unsatisfactory performance regarding challenging small targets, insufficient training data, high annotation cost, the lack of domain-specific knowledge, etc. These challenges cultivate the need for developing data-efficient and knowledge-aware deep learning techniques which can generalize to different medical tasks without requiring intensive manual labeling efforts, and incorporate domain-specific knowledge in the learning process. In this thesis, we rethink the current progress of deep learning in medical image analysis, with a focus on the aforementioned challenges, and present different data-efficient and knowledge-aware deep learning approaches to address them accordingly. Firstly, we introduce coarse-to-fine mechanisms which use the prediction from the first (coarse) stage to shrink the input region for the second (fine) stage, to enhance the model performance especially for segmenting small challenging structures, such as the pancreas which occupies only a very small fraction (e.g., < 0.5%) of the entire CT volume. The method achieved the state-of-the-art result on the NIH pancreas segmentation dataset. Further extensions also demonstrated effectiveness for segmenting neoplasms such as pancreatic cysts or multiple organs. Secondly, we present a semi-supervised learning framework for medical image segmentation by leveraging both limited labeled data and abundant unlabeled data. Our learning method encourages the segmentation output to be consistent for the same input under different viewing conditions. More importantly, the outputs from different viewing directions are fused altogether to improve the quality of the target, which further enhances the overall performance. The comparison with fully-supervised methods on multi-organ segmentation confirms the effectiveness of this method. Thirdly, we discuss how to incorporate knowledge priors for multi-organ segmentation. Noticing that the abdominal organ sizes exhibit similar distributions across different cohorts, we propose to explicitly incorporate anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. The approach achieves 84.97% on the MICCAI 2015 challenge “Multi-Atlas Labeling Beyond the Cranial Vault”, which significantly outperforms previous state-of-the-art even using fewer annotations. Lastly, by rethinking how radiologists interpret medical images, we identify one limitation for existing deep-learning-based works on detecting pancreatic ductal adenocarcinoma is the lack of knowledge integration from multi-phase images. Thereby, we introduce a dual-path network where different paths are connected for multi-phase information exchange, and an additional loss is added for removing view divergence. By effectively incorporating multi-phase information, the presented method shows superior performance than prior arts on this matter