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Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey

arXiv 2108.08467 ↗First posted 19 Aug 2021eess.IV cs.CV cs.LGcited by 16

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Abstract (arXiv)

Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 66 of its 135 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

66 of 135 references.

References · 66

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38 more
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Citing papers per year: 2022: 3; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

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