Deep Learning in Multi-organ Segmentation
It provides a comprehensive overview for researchers and practitioners in medical imaging, but it is incremental as it synthesizes existing work rather than introducing new methods.
This paper reviews deep learning methods for multi-organ segmentation in medical images, summarizing and categorizing approaches while comparing them on benchmark datasets like the 2017 AAPM Thoracic and 2015 MICCAI Head Neck challenges.
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.