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 grounds for technological development of 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, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
Highlights
Owning to the recent rise of high-resolution imaging modalities such as X-ray computed tomography (CT) and magnetic resonance imaging (MRI), medical practitioners rely on spatial visualization of internal organs to evaluate disease and make timely clinical decisions
Unlike traditional machine learning (ML) approaches that rely on pre-programmed sets of instructions and manually-curated input data, deep learning (DL) offers the possibility of automatic feature extraction and learning from “raw data”
The literature review in this study was conducted by an initial article search in PubMed/Medline and ScienceDirect databases with the keywords “deep learning”, “segmentation”, “cancer”, “organs at risk”, “radiation oncology”, “radiology” and “radiotherapy”, and a subsequent manual reference check of the relevant publications
Summary
Owning to the recent rise of high-resolution imaging modalities such as X-ray computed tomography (CT) and magnetic resonance imaging (MRI), medical practitioners rely on spatial visualization of internal organs to evaluate disease and make timely clinical decisions. Even though radiological assessment of imaging studies is still largely visual and based on domain knowledge and expertise, there is an increasing shift towards quantitative and volumetric disease assessment for precision medicine [1,2] This step requires accurate tissue segmentation, which can improve disease characterization through detection and division of abnormalities on images into semantically, biologically and/or clinically meaningful regions based on quantitative imaging measurements. The segmentation of organs-at-risk (OARs) and target volumes are necessary steps to aid the planning of optimal dose delivery to tumors while avoiding delivering toxicity to surrounding healthy tissues Accurate segmentation of these structures is vital during radiotherapy (RT) for effective image-guided treatment. Our multidisciplinary team provides an up-to-date overview of the current DL techniques used for pelvic cancer segmentation, pinpoints key achievements and discusses limitations for potential adoption in clinical practice
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