Block Level Skip Connections across Cascaded V-Net for Multi-Organ Segmentation

Liang Zhang, Jiaming Zhang, Peiyi Shen, Guangming Zhu, Ping Li, Xiaoyuan Lu, Huan Zhang, Syed Afaq Shah, Mohammed Bennamoun

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.63 percents on dice score, and four organs in Multi-Atlas Labeling Beyond the Cranial Vault challenge have achieved a gain of 5.27 percents on average dice score.

Original languageEnglish
Article number9006924
Pages (from-to)2782-2793
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - Sept 2020


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