DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search

• URL: http://arxiv.org/abs/2109.09271v1
• Date: Mon, 20 Sep 2021 02:32:50 GMT
• Title: DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
• Authors: Dazhou Guo, Xianghua Ye, Jia Ge, Xing Di, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Zhongjie Liu, Ling Peng, Senxiang Yan, Dakai Jin
• Abstract summary: Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. We formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs.
• Score: 13.642187665173427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without voxel-level supervision, the performance is severely limited. LNS is highly context-dependent - LNS boundaries are constrained by anatomical organs - we formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs. This permits the deep network to better learn from both CT appearance and organ context. We develop a stratified referencing organ segmentation protocol that divides the organs into anchor and non-anchor categories and uses the former's predictions to guide the later segmentation. We further develop an auto-search module to identify the key organs that opt for the optimal LNS parsing performance. Extensive four-fold cross-validation experiments on a dataset of 98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22 organs in thoracic region to date) are conducted. Our LNS parsing model produces significant performance improvements, with an average Dice score of 81.1% +/- 6.1%, which is 5.0% and 19.2% higher over the pure CT-based deep model and the previous representative approach, respectively.

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