Application of belief functions to medical image segmentation: A review

Ling, Hoe I.; Ruan, Su

doi:10.1016/j.inffus.2022.11.008

Cited by 18 publications

(4 citation statements)

References 145 publications

(273 reference statements)

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“…Quantifying network segmentation uncertainty is a crucial aspect of medical image segmentation ( 37 , 77 , 96 - 98 ). To describe the uncertainty of the segmentation task, De Biase et al ( 77 ) utilized generative probability maps.…”

Section: Discussionmentioning

confidence: 99%

“…Attention mechanisms such as channel and spatial attention ( 84 ), SE module ( 60 , 86 ), and transformer attention ( 89 ) are also discussed. Uncertainty analysis techniques ( 37 , 77 , 96 - 98 ) are used to quantify segmentation uncertainty and improve segmentation results. Tables 5-7 summarize the segmentation methods for BraTS, HECKTOR dataset, and whole-body tumor dataset, respectively, including the fusion methods of different modal data, the network structure used, the data processing method, and the segmentation results.…”

Section: Discussionmentioning

confidence: 99%

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Multi-modal tumor segmentation methods based on deep learning: a narrative review

Xue,

Yao,

Teng

2024

Quant Imaging Med Surg

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Background and Objective Automatic tumor segmentation is a critical component in clinical diagnosis and treatment. Although single-modal imaging provides useful information, multi-modal imaging provides a more comprehensive understanding of the tumor. Multi-modal tumor segmentation has been an essential topic in medical image processing. With the remarkable performance of deep learning (DL) methods in medical image analysis, multi-modal tumor segmentation based on DL has attracted significant attention. This study aimed to provide an overview of recent DL-based multi-modal tumor segmentation methods. Methods In in the PubMed and Google Scholar databases, the keywords “multi-modal”, “deep learning”, and “tumor segmentation” were used to systematically search English articles in the past 5 years. The date range was from 1 January 2018 to 1 June 2023. A total of 78 English articles were reviewed. Key Content and Findings We introduce public datasets, evaluation methods, and multi-modal data processing. We also summarize common DL network structures, techniques, and multi-modal image fusion methods used in different tumor segmentation tasks. Finally, we conclude this study by presenting perspectives for future research. Conclusions In multi-modal tumor segmentation tasks, DL technique is a powerful method. With the fusion methods of different modal data, the DL framework can effectively use the characteristics of different modal data to improve the accuracy of tumor segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Xue,

Yao,

Teng

2024

Quant Imaging Med Surg

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“…Many reviews have been published on automatic segmentation methods for specific regions of interest [17][18][19][20][21][22][23][24][25][26], image modalities [17,20,24,27,28], and methods [29][30][31][32][33][34][35][36][37][38][39][40]. How-ever, none of these thoroughly cover all three aspects to provide a holistic overview of the state of the field.…”

Section: Related Workmentioning

confidence: 99%

Automatic Segmentation with Deep Learning in Radiotherapy

Isaksson,

Summers,

Mastroleo

et al. 2023

Cancers

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This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.

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“…Apart from using discounting operations to generate mass functions, another solution is to construct deep evidential segmentation frameworks to output mass functions directly and then combine multiple pieces of evidence [21], [22]. More details about the DST-based multimodal medical image segmentation can be found in [23].…”

Section: Multimodal Medical Image Segmentationmentioning

confidence: 99%

Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation

Ling

Denœux

Véra³

et al. 2022

Lecture Notes in Computer Science

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Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases based on multimodal medical images such as, e.g., PET/CT. The effective fusion of multimodal information is essential to reach a reliable decision and explain how the decision is made as well. In this paper, we propose a fusion framework for multimodal medical image segmentation based on deep learning and the Dempster-Shafer theory of evidence. In this framework, the reliability of each single modality image when segmenting different objects is taken into account by a contextual discounting operation. The discounted pieces of evidence from each modality are then combined by Dempster's rule to reach a final decision. Experimental results with a PET-CT dataset with lymphomas and a multi-MRI dataset with brain tumors show that our method outperforms the state-of-the-art methods in accuracy and reliability.

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Application of belief functions to medical image segmentation: A review

Cited by 18 publications

References 145 publications

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Automatic Segmentation with Deep Learning in Radiotherapy

Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation

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