Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges
Lymphoma is a prevalent malignant tumor within the hematological system, posing significant challenges to clinical practice due to its diverse subtypes, intricate radiological and metabolic manifestations. Lymphoma segmentation studies based on positron emission tomography/computed tomography (PET/C...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251362508 |
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| _version_ | 1849729703565328384 |
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| author | Wanru Liang Feiyang Yang Peihong Teng Tianyang Zhang Weizhang Shen |
| author_facet | Wanru Liang Feiyang Yang Peihong Teng Tianyang Zhang Weizhang Shen |
| author_sort | Wanru Liang |
| collection | DOAJ |
| description | Lymphoma is a prevalent malignant tumor within the hematological system, posing significant challenges to clinical practice due to its diverse subtypes, intricate radiological and metabolic manifestations. Lymphoma segmentation studies based on positron emission tomography/computed tomography (PET/CT), CT, and magnetic resonance imaging represent key strategies for addressing these challenges. This article reviews the advancements in lymphoma segmentation research utilizing deep learning methods, offering a comparative analysis with traditional approaches, and conducting an in-depth examination and summary of aspects such as dataset characteristics, backbone networks of models, adjustments to network structures based on research objectives, and model performance. The article also explores the potential and challenges of translating deep learning-based lymphoma segmentation research into clinical scenarios, with a focus on practical clinical applications. The future research priorities in lymphoma segmentation are identified as enhancing the models’ clinical generalizability, integrating into clinical workflows, reducing computational demands, and expanding high-quality datasets. These efforts aim to facilitate the broad application of deep learning in the diagnosis and treatment monitoring of lymphoma. |
| format | Article |
| id | doaj-art-e0332fb47402410f9caee3ae78d540e3 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-e0332fb47402410f9caee3ae78d540e32025-08-20T03:09:07ZengSAGE PublishingDigital Health2055-20762025-07-011110.1177/20552076251362508Recent advances in deep learning for lymphoma segmentation: Clinical applications and challengesWanru Liang0Feiyang Yang1Peihong Teng2Tianyang Zhang3Weizhang Shen4 Department of Hematology and Oncology, , Changchun, China College of Computer Science and Technology, , Changchun, China Department of Radiology, , Changchun, China Department of General Surgery Center Hepatobiliary and Pancreatic Surgery, , Changchun, China Department of Hematology and Oncology, , Changchun, ChinaLymphoma is a prevalent malignant tumor within the hematological system, posing significant challenges to clinical practice due to its diverse subtypes, intricate radiological and metabolic manifestations. Lymphoma segmentation studies based on positron emission tomography/computed tomography (PET/CT), CT, and magnetic resonance imaging represent key strategies for addressing these challenges. This article reviews the advancements in lymphoma segmentation research utilizing deep learning methods, offering a comparative analysis with traditional approaches, and conducting an in-depth examination and summary of aspects such as dataset characteristics, backbone networks of models, adjustments to network structures based on research objectives, and model performance. The article also explores the potential and challenges of translating deep learning-based lymphoma segmentation research into clinical scenarios, with a focus on practical clinical applications. The future research priorities in lymphoma segmentation are identified as enhancing the models’ clinical generalizability, integrating into clinical workflows, reducing computational demands, and expanding high-quality datasets. These efforts aim to facilitate the broad application of deep learning in the diagnosis and treatment monitoring of lymphoma.https://doi.org/10.1177/20552076251362508 |
| spellingShingle | Wanru Liang Feiyang Yang Peihong Teng Tianyang Zhang Weizhang Shen Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges Digital Health |
| title | Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges |
| title_full | Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges |
| title_fullStr | Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges |
| title_full_unstemmed | Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges |
| title_short | Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges |
| title_sort | recent advances in deep learning for lymphoma segmentation clinical applications and challenges |
| url | https://doi.org/10.1177/20552076251362508 |
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