HighlightsThis work presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.This work introduces the related information to the challenge, discusses the results from the conventional methods and deep learning-based algorithms, and provides insights to the future research.The challenge provides a fair and intuitive comparison framework for methods developed and being developed for WHS.The challenge provides the training datasets with manually delineated ground truths and evaluation for an ongoing development of MM-WHS algorithms.

This is a repository copy of A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

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Volumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity and complex anatomy shapes conspire towards a great lack of efficient tools for the segmentation. It makes 3D ultrasound difficult to interpret and hinders the widespread of 3D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: i) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac and placenta, which remains a rarely-studied and arduous challenge; ii) we propose a composite architecture for dense labeling, in which a customized 3D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; iii) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large datasets, our method illustrates superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations.

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