Characterization of adrenal glands on computed tomography with a 3D V-Net-based model
Abstract Objectives To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal. Methods A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of...
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SpringerOpen
2025-01-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01898-7 |
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| author | Yuanchong Chen Yaofeng Zhang Xiaodong Zhang Xiaoying Wang |
| author_facet | Yuanchong Chen Yaofeng Zhang Xiaodong Zhang Xiaoying Wang |
| author_sort | Yuanchong Chen |
| collection | DOAJ |
| description | Abstract Objectives To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal. Methods A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes. Results The DSC of the test set of the segmentation model was 0.900 (0.810–0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively). Conclusion The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands. Critical relevance statement A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene. Key Points Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes. Graphical Abstract |
| format | Article |
| id | doaj-art-d73fdeebe08a4a8b99fd5ce26f5f63b2 |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-d73fdeebe08a4a8b99fd5ce26f5f63b22025-01-19T12:26:12ZengSpringerOpenInsights into Imaging1869-41012025-01-011611910.1186/s13244-025-01898-7Characterization of adrenal glands on computed tomography with a 3D V-Net-based modelYuanchong Chen0Yaofeng Zhang1Xiaodong Zhang2Xiaoying Wang3Department of Radiology, Peking University First HospitalBeijing Smart Tree Medical Technology Co. Ltd.Department of Radiology, Peking University First HospitalDepartment of Radiology, Peking University First HospitalAbstract Objectives To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal. Methods A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes. Results The DSC of the test set of the segmentation model was 0.900 (0.810–0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively). Conclusion The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands. Critical relevance statement A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene. Key Points Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01898-7Adrenal glandComputed tomographyDeep learningSegmentationClassification |
| spellingShingle | Yuanchong Chen Yaofeng Zhang Xiaodong Zhang Xiaoying Wang Characterization of adrenal glands on computed tomography with a 3D V-Net-based model Insights into Imaging Adrenal gland Computed tomography Deep learning Segmentation Classification |
| title | Characterization of adrenal glands on computed tomography with a 3D V-Net-based model |
| title_full | Characterization of adrenal glands on computed tomography with a 3D V-Net-based model |
| title_fullStr | Characterization of adrenal glands on computed tomography with a 3D V-Net-based model |
| title_full_unstemmed | Characterization of adrenal glands on computed tomography with a 3D V-Net-based model |
| title_short | Characterization of adrenal glands on computed tomography with a 3D V-Net-based model |
| title_sort | characterization of adrenal glands on computed tomography with a 3d v net based model |
| topic | Adrenal gland Computed tomography Deep learning Segmentation Classification |
| url | https://doi.org/10.1186/s13244-025-01898-7 |
| work_keys_str_mv | AT yuanchongchen characterizationofadrenalglandsoncomputedtomographywitha3dvnetbasedmodel AT yaofengzhang characterizationofadrenalglandsoncomputedtomographywitha3dvnetbasedmodel AT xiaodongzhang characterizationofadrenalglandsoncomputedtomographywitha3dvnetbasedmodel AT xiaoyingwang characterizationofadrenalglandsoncomputedtomographywitha3dvnetbasedmodel |