Accurate object localization facilitates automatic esophagus segmentation in deep learning.

Journal: Radiation oncology (London, England)
PMID: 38735947

Abstract

BACKGROUND: Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task.

Authors

  • Zhibin Li
    Epidemiology Research Unit, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com.
  • Guanghui Gan
    Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Jian Guo
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China.
  • Wei Zhan
    Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Long Chen
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

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