Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Version 1 : Received: 10 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (12:17:31 CEST)

A peer-reviewed article of this Preprint also exists.

Kalantar, R.; Lin, G.; Winfield, J.M.; Messiou, C.; Lalondrelle, S.; Blackledge, M.D.; Koh, D.-M. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics 2021, 11, 1964. Kalantar, R.; Lin, G.; Winfield, J.M.; Messiou, C.; Lalondrelle, S.; Blackledge, M.D.; Koh, D.-M. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics 2021, 11, 1964.

Journal reference: Diagnostics 2021, 11, 1964
DOI: 10.3390/diagnostics11111964

Abstract

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ground for technology development for computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive and clinically-oriented overview of DL-based segmentation studies for bladder, prostate, cervical and rectal cancers, highlighting the key findings, challenges and limitations.

Keywords

Deep Learning; Pelvic Cancer Segmentation; Radiology; Radiation Oncology; Radiotherapy Planning

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