Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning
Abstract To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs with a short-axis diameter ≥ 3 mm from 626 head and neck cancer patients across four hospit...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84804-3 |
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| author | Wenjun Liao Xiangde Luo Lu Li Jinfeng Xu Yuan He Hui Huang Shichuan Zhang |
| author_facet | Wenjun Liao Xiangde Luo Lu Li Jinfeng Xu Yuan He Hui Huang Shichuan Zhang |
| author_sort | Wenjun Liao |
| collection | DOAJ |
| description | Abstract To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs with a short-axis diameter ≥ 3 mm from 626 head and neck cancer patients across four hospitals. The nnUNet model was used as a baseline, pre-trained on a large-scale head and neck dataset, and then fine-tuned with 4,729 LNs from hospital A for detection and segmentation. Validation was conducted on an internal testing cohort (ITC A) and three external testing cohorts (ETCs B, C, and D), with 1684 and 4600 LNs, respectively. Detection was evaluated via sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), while segmentation was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD95). For detection, the sensitivity, PPV, and FP/vol in ITC A were 54.6%, 69.0%, and 3.4, respectively. In ETCs, the sensitivity ranged from 45.7% at 3.9 FP/vol to 63.5% at 5.8 FP/vol. Segmentation achieved a mean DSC of 0.72 in ITC A and 0.72 to 0.74 in ETCs, as well as a mean HD95 of 3.78 mm in ITC A and 2.73 mm to 2.85 mm in ETCs. No significant sensitivity difference was found between contrast-enhanced and unenhanced CT images (p = 0.502) or repeated CT images (p = 0.815) during adaptive radiotherapy. The model’s segmentation accuracy was comparable to that of experienced oncologists. The model shows promise in automatically detecting and segmenting neck LNs in CT images, potentially reducing oncologists’ segmentation workload. |
| format | Article |
| id | doaj-art-0455afa87a1543539d8a3384914b8c56 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0455afa87a1543539d8a3384914b8c562025-02-09T12:29:31ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-024-84804-3Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learningWenjun Liao0Xiangde Luo1Lu Li2Jinfeng Xu3Yuan He4Hui Huang5Shichuan Zhang6Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Cancer Hospital Affiliate to School of Medicine, University of Electronic Science and Technology of ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of ChinaDepartment of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Cancer Hospital Affiliate to School of Medicine, University of Electronic Science and Technology of ChinaDepartment of Radiation Oncology, Nanfang Hospital, Southern Medical UniversityDepartment of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaCancer Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Cancer Hospital Affiliate to School of Medicine, University of Electronic Science and Technology of ChinaAbstract To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs with a short-axis diameter ≥ 3 mm from 626 head and neck cancer patients across four hospitals. The nnUNet model was used as a baseline, pre-trained on a large-scale head and neck dataset, and then fine-tuned with 4,729 LNs from hospital A for detection and segmentation. Validation was conducted on an internal testing cohort (ITC A) and three external testing cohorts (ETCs B, C, and D), with 1684 and 4600 LNs, respectively. Detection was evaluated via sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), while segmentation was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD95). For detection, the sensitivity, PPV, and FP/vol in ITC A were 54.6%, 69.0%, and 3.4, respectively. In ETCs, the sensitivity ranged from 45.7% at 3.9 FP/vol to 63.5% at 5.8 FP/vol. Segmentation achieved a mean DSC of 0.72 in ITC A and 0.72 to 0.74 in ETCs, as well as a mean HD95 of 3.78 mm in ITC A and 2.73 mm to 2.85 mm in ETCs. No significant sensitivity difference was found between contrast-enhanced and unenhanced CT images (p = 0.502) or repeated CT images (p = 0.815) during adaptive radiotherapy. The model’s segmentation accuracy was comparable to that of experienced oncologists. The model shows promise in automatically detecting and segmenting neck LNs in CT images, potentially reducing oncologists’ segmentation workload.https://doi.org/10.1038/s41598-024-84804-3Head and neck cancerNeck lymph nodeDeep learningDetectionSegmentation |
| spellingShingle | Wenjun Liao Xiangde Luo Lu Li Jinfeng Xu Yuan He Hui Huang Shichuan Zhang Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning Scientific Reports Head and neck cancer Neck lymph node Deep learning Detection Segmentation |
| title | Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| title_full | Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| title_fullStr | Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| title_full_unstemmed | Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| title_short | Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| title_sort | automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning |
| topic | Head and neck cancer Neck lymph node Deep learning Detection Segmentation |
| url | https://doi.org/10.1038/s41598-024-84804-3 |
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