Deep Learning in Multi-organ Segmentation

Yang Lei; Yabo Fu; Tonghe Wang; Richard L. J. Qiu; Walter J. Curran; Tian Liu; Xiaofeng Yang

Deep Learning in Multi-organ Segmentation

Image and Video Processing 2020-01-30 v1 Computer Vision and Pattern Recognition Medical Physics

Authors: Yang Lei , Yabo Fu , Tonghe Wang , Richard L. J. Qiu , Walter J. Curran , Tian Liu , Xiaofeng Yang

Abstract

This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.

Keywords

medical image segmentation deep learning segmentation image segmentation

Cite

@article{arxiv.2001.10619,
  title  = {Deep Learning in Multi-organ Segmentation},
  author = {Yang Lei and Yabo Fu and Tonghe Wang and Richard L. J. Qiu and Walter J. Curran and Tian Liu and Xiaofeng Yang},
  journal= {arXiv preprint arXiv:2001.10619},
  year   = {2020}
}

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37 pages, 2 figures, 8 tables

Deep Learning in Multi-organ Segmentation

Abstract

Keywords

Cite

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