Deep Learning in Multi-organ Segmentation

• URL: http://arxiv.org/abs/2001.10619v1
• Date: Tue, 28 Jan 2020 22:11:44 GMT
• Title: Deep Learning in Multi-organ Segmentation
• Authors: Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
• Abstract summary: We summarized the latest DL-based methods for medical image segmentation and applications. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets.
• Score: 2.3467691785656557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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