Deep learning-based segmentation of 3D ultrasound images of the thyroid
The goal of the study was to develop a method for segmentation of the thyroid, carotid artery (CA), and jugular vein (JV) using 3D ultrasound data. This method forms the basis for a computer-assisted needle-based intervention for thyroid nodules and thyroid volume estimation accuracy. Two datasets w...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | WFUMB Ultrasound Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949668324000235 |
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| author | Roxane Munsterman Tim Boers Sicco J. Braak Jelmer M. Wolterink Michel Versluis Srirang Manohar |
| author_facet | Roxane Munsterman Tim Boers Sicco J. Braak Jelmer M. Wolterink Michel Versluis Srirang Manohar |
| author_sort | Roxane Munsterman |
| collection | DOAJ |
| description | The goal of the study was to develop a method for segmentation of the thyroid, carotid artery (CA), and jugular vein (JV) using 3D ultrasound data. This method forms the basis for a computer-assisted needle-based intervention for thyroid nodules and thyroid volume estimation accuracy. Two datasets were used: the first was acquired using a tracked 2D sweep and the second with a 3D matrix transducer. A 2D and 3D U-Net model were trained on the full data set with different strategies (2D, majority vote in 2.5D and 3D). The 2D model achieved the best results for the tracked 2D sweep data set in terms of median Dice Score Coefficient (DSC) (0.934, 0.924, 0.897) and Hausdorff distance at the 95 percentile (HD95) (1.206, 0.588, 1.571 mm) for the thyroid, CA, and JV, respectively. For the matrix data set, the 3D model gave overall the best results in its median DSC (0.869, 0.930, 0.856) and HD95 (1.814, 0.606, 1.405 mm) for the thyroid, CA, and JV, respectively, showing comparable results in vessel segmentation but inferior results in thyroid segmentation compared to the tracked sweep data set. The model demonstrated lower median volume estimation errors in the tracked sweep data set (4.45 %) compared to the matrix data set (7.40 %) and the ellipsoid formula (13.84 %) for thyroid volume estimation. This work shows that automatic segmentation in 3D ultrasound of the human neck is best performed with 3D ultrasound. Improving the quality of the 3D data is important for the development of a planning and navigation method to be used with needle-based interventions for thyroid nodules. |
| format | Article |
| id | doaj-art-abf7a7bcd2e141c3bb14730522b9c213 |
| institution | Kabale University |
| issn | 2949-6683 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | WFUMB Ultrasound Open |
| spelling | doaj-art-abf7a7bcd2e141c3bb14730522b9c2132024-12-13T11:09:13ZengElsevierWFUMB Ultrasound Open2949-66832024-12-0122100055Deep learning-based segmentation of 3D ultrasound images of the thyroidRoxane Munsterman0Tim Boers1Sicco J. Braak2Jelmer M. Wolterink3Michel Versluis4Srirang Manohar5Multi-Modality Medical Imaging Group, TechMed Centre, University of Twente, Enschede, the NetherlandsMulti-Modality Medical Imaging Group, TechMed Centre, University of Twente, Enschede, the Netherlands; Corresponding author. Drienerlolaan 5, 7522NB, Enschede, the Netherlands.Department of Radiology, Ziekenhuisgroep Twente, Hengelo, the NetherlandsDepartment of Applied Mathematics, TechMed Centre, University of Twente, Enschede, the NetherlandsPhysics of Fluids Group, TechMed Centre, University of Twente, Enschede, the NetherlandsMulti-Modality Medical Imaging Group, TechMed Centre, University of Twente, Enschede, the NetherlandsThe goal of the study was to develop a method for segmentation of the thyroid, carotid artery (CA), and jugular vein (JV) using 3D ultrasound data. This method forms the basis for a computer-assisted needle-based intervention for thyroid nodules and thyroid volume estimation accuracy. Two datasets were used: the first was acquired using a tracked 2D sweep and the second with a 3D matrix transducer. A 2D and 3D U-Net model were trained on the full data set with different strategies (2D, majority vote in 2.5D and 3D). The 2D model achieved the best results for the tracked 2D sweep data set in terms of median Dice Score Coefficient (DSC) (0.934, 0.924, 0.897) and Hausdorff distance at the 95 percentile (HD95) (1.206, 0.588, 1.571 mm) for the thyroid, CA, and JV, respectively. For the matrix data set, the 3D model gave overall the best results in its median DSC (0.869, 0.930, 0.856) and HD95 (1.814, 0.606, 1.405 mm) for the thyroid, CA, and JV, respectively, showing comparable results in vessel segmentation but inferior results in thyroid segmentation compared to the tracked sweep data set. The model demonstrated lower median volume estimation errors in the tracked sweep data set (4.45 %) compared to the matrix data set (7.40 %) and the ellipsoid formula (13.84 %) for thyroid volume estimation. This work shows that automatic segmentation in 3D ultrasound of the human neck is best performed with 3D ultrasound. Improving the quality of the 3D data is important for the development of a planning and navigation method to be used with needle-based interventions for thyroid nodules.http://www.sciencedirect.com/science/article/pii/S29496683240002353D ultrasoundMatrix transducerDeep learningSegmentationThyroidPre-operative planning |
| spellingShingle | Roxane Munsterman Tim Boers Sicco J. Braak Jelmer M. Wolterink Michel Versluis Srirang Manohar Deep learning-based segmentation of 3D ultrasound images of the thyroid WFUMB Ultrasound Open 3D ultrasound Matrix transducer Deep learning Segmentation Thyroid Pre-operative planning |
| title | Deep learning-based segmentation of 3D ultrasound images of the thyroid |
| title_full | Deep learning-based segmentation of 3D ultrasound images of the thyroid |
| title_fullStr | Deep learning-based segmentation of 3D ultrasound images of the thyroid |
| title_full_unstemmed | Deep learning-based segmentation of 3D ultrasound images of the thyroid |
| title_short | Deep learning-based segmentation of 3D ultrasound images of the thyroid |
| title_sort | deep learning based segmentation of 3d ultrasound images of the thyroid |
| topic | 3D ultrasound Matrix transducer Deep learning Segmentation Thyroid Pre-operative planning |
| url | http://www.sciencedirect.com/science/article/pii/S2949668324000235 |
| work_keys_str_mv | AT roxanemunsterman deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid AT timboers deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid AT siccojbraak deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid AT jelmermwolterink deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid AT michelversluis deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid AT srirangmanohar deeplearningbasedsegmentationof3dultrasoundimagesofthethyroid |