Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana, Iromi R.; Stirling, David; Ros, Montserrat; Field, Matthew

doi:10.3390/cancers15061750

Cited by 4 publications

(1 citation statement)

References 124 publications

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“…The statistical significance in DSC improvement for papers which excluded cross validation compared to those which included it indicates the potential of an overfitting problem in these cases, highlighting the importance of test sets and external validation for generalizability. While other reviews have investigated similar segmentation performance tasks applied to various lesions or whole organs, to the best of our knowledge, ours is the first to focus on deep learning techniques applied specifically to lesions of the bone ( 70 – 77 ). Additionally, ours is the first which specifically evaluates differences in segmentation performance specifically as they relate to imaging modality, imaging dimensionality, and predominant lesion characteristic.…”

Section: Discussionmentioning

confidence: 99%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

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IntroductionImage segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).MethodThe literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.ResultsThe majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9.DiscussionDeep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

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Section: Discussionmentioning

confidence: 99%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

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The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

Ataei,

Eggermont,

Verdonschot

et al. 2024

Bone

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AI‐powered automated analysis of bone scans: A survey

Lin,

He,

Guo

2024

IET Image Processing

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As one of the key techniques of artificial intelligence, deep learning has emerged as an effective approach for analysing medical images. Various imaging techniques including the planar bone scintigraphy, single photon emission computed tomography and PET can be used to evaluate, in vivo, bone conditions. The introduction of deep learning techniques especially the convolutional neural networks can significantly improve diagnosis accuracy and efficiency of nuclear medicine physicians. Focusing on bone scans acquired by various nuclear medicine imaging techniques, his paper reviews existing work on deep learning‐based classification, segmentation and object detection of bone scans. Specifically, an overview of existing work about research objective is presented, deep learning models are adopted, and main results are achieved. Research challenges and directions for developing automated analysis of bone scans with deep learning techniques are then discussed.

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Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Cited by 4 publications

References 124 publications

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

AI‐powered automated analysis of bone scans: A survey

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