A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910364165210112 |
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| author | Ahamed, Shadab Dubljevic, Natalia Bloise, Ingrid Gowdy, Claire Martineau, Patrick Wilson, Don Uribe, Carlos F. Rahmim, Arman Yousefirizi, Fereshteh |
| author_facet | Ahamed, Shadab Dubljevic, Natalia Bloise, Ingrid Gowdy, Claire Martineau, Patrick Wilson, Don Uribe, Carlos F. Rahmim, Arman Yousefirizi, Fereshteh |
| contents | Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. As compared to a single end-to-end network for segmentation of tumors in whole-body PET images, our three-step model is more effective (improves 3D Dice score from 58.9% to 78.1%) since each of its specialized modules, namely the slice classifier, the tumor detector and the tumor segmentor, can be trained independently to a high degree of skill to carry out a specific task, rather than a single network with suboptimal performance on overall segmentation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_07092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma Ahamed, Shadab Dubljevic, Natalia Bloise, Ingrid Gowdy, Claire Martineau, Patrick Wilson, Don Uribe, Carlos F. Rahmim, Arman Yousefirizi, Fereshteh Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. As compared to a single end-to-end network for segmentation of tumors in whole-body PET images, our three-step model is more effective (improves 3D Dice score from 58.9% to 78.1%) since each of its specialized modules, namely the slice classifier, the tumor detector and the tumor segmentor, can be trained independently to a high degree of skill to carry out a specific task, rather than a single network with suboptimal performance on overall segmentation. |
| title | A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2403.07092 |