An interactive deep-learning workflow for head and neck gross tumour volume segmentation
Background and purpose: Deep learning (DL)-based auto-segmentation of head and neck cancer (HNC) gross tumour volumes remains challenging due to anatomical complexity and limited accuracy. We propose an interactive DL (iDL) workflow that integrates clinician input to enhance segmentation performance...
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| Format: | Article |
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Elsevier
2025-07-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631625001253 |
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| author | Zixiang Wei Jintao Ren Jesper Grau Eriksen Kenneth Jensen Hanna Rahbek Mortensen Stine Sofia Korreman Jasper Nijkamp |
| author_facet | Zixiang Wei Jintao Ren Jesper Grau Eriksen Kenneth Jensen Hanna Rahbek Mortensen Stine Sofia Korreman Jasper Nijkamp |
| author_sort | Zixiang Wei |
| collection | DOAJ |
| description | Background and purpose: Deep learning (DL)-based auto-segmentation of head and neck cancer (HNC) gross tumour volumes remains challenging due to anatomical complexity and limited accuracy. We propose an interactive DL (iDL) workflow that integrates clinician input to enhance segmentation performance and clinical usability. The iDL approach was evaluated using simulations on two datasets and an observer study. Materials and methods: Two iDL approaches were developed and integrated into a workflow: (1) for primary tumour (GTVt) segmentation, clinicians marked the tumour centre and delineated three orthogonal slices to enable patient-specific fine-tuning of a pre-trained 3D UNet; (2) for lymph nodes (GTVn), clinician-provided clicks identified involved lymph nodes used as attention maps in a separate UNet. Methods were evaluated using independent simulations on an internal dataset (n = 204) and the HECKTOR 2022 dataset (n = 524), using aggregated dice-similarity-coefficient (DSCagg). An additional observer study with three radiation oncologists assessed usability and efficiency, using normalized added path length (APL) and the System Usability Scale (SUS). Results: The iDL workflow achieved high segmentation accuracy, with a DSCagg of 0.84 (internal) and 0.88 (HECKTOR) for GTVt, and 0.83 and 0.85 for GTVn. GTVn required minimal correction (mean APL: 4 % vs. 6 % vs. 11 %); two observers made limited corrections to GTVt (mean APL: 11 % vs. 6 % vs. 39 %). Mean segmentation time was 12 min per case. SUS scores ranged from 87.5 to 100, indicating high usability. Conclusion: The iDL workflow achieved high accuracy and usability with limited correction time, offering a practical and efficient solution for HNC segmentation in radiotherapy. |
| format | Article |
| id | doaj-art-cc8af8984e40468c91911964762f9a5a |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-cc8af8984e40468c91911964762f9a5a2025-08-20T03:40:31ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-07-013510082010.1016/j.phro.2025.100820An interactive deep-learning workflow for head and neck gross tumour volume segmentationZixiang Wei0Jintao Ren1Jesper Grau Eriksen2Kenneth Jensen3Hanna Rahbek Mortensen4Stine Sofia Korreman5Jasper Nijkamp6Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, DenmarkDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, DenmarkDepartment of Oncology, Aarhus University Hospital, Aarhus, Denmark; Department of Experimental Oncology, Aarhus University Hospital, DenmarkDanish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, DenmarkDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, DenmarkDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, DenmarkDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; Corresponding author at: Palle Juul-Jensens Blvd. 25, 8200 Aarhus, Denmark.Background and purpose: Deep learning (DL)-based auto-segmentation of head and neck cancer (HNC) gross tumour volumes remains challenging due to anatomical complexity and limited accuracy. We propose an interactive DL (iDL) workflow that integrates clinician input to enhance segmentation performance and clinical usability. The iDL approach was evaluated using simulations on two datasets and an observer study. Materials and methods: Two iDL approaches were developed and integrated into a workflow: (1) for primary tumour (GTVt) segmentation, clinicians marked the tumour centre and delineated three orthogonal slices to enable patient-specific fine-tuning of a pre-trained 3D UNet; (2) for lymph nodes (GTVn), clinician-provided clicks identified involved lymph nodes used as attention maps in a separate UNet. Methods were evaluated using independent simulations on an internal dataset (n = 204) and the HECKTOR 2022 dataset (n = 524), using aggregated dice-similarity-coefficient (DSCagg). An additional observer study with three radiation oncologists assessed usability and efficiency, using normalized added path length (APL) and the System Usability Scale (SUS). Results: The iDL workflow achieved high segmentation accuracy, with a DSCagg of 0.84 (internal) and 0.88 (HECKTOR) for GTVt, and 0.83 and 0.85 for GTVn. GTVn required minimal correction (mean APL: 4 % vs. 6 % vs. 11 %); two observers made limited corrections to GTVt (mean APL: 11 % vs. 6 % vs. 39 %). Mean segmentation time was 12 min per case. SUS scores ranged from 87.5 to 100, indicating high usability. Conclusion: The iDL workflow achieved high accuracy and usability with limited correction time, offering a practical and efficient solution for HNC segmentation in radiotherapy.http://www.sciencedirect.com/science/article/pii/S2405631625001253Interactive deep-learningHead and neck cancerObserver study |
| spellingShingle | Zixiang Wei Jintao Ren Jesper Grau Eriksen Kenneth Jensen Hanna Rahbek Mortensen Stine Sofia Korreman Jasper Nijkamp An interactive deep-learning workflow for head and neck gross tumour volume segmentation Physics and Imaging in Radiation Oncology Interactive deep-learning Head and neck cancer Observer study |
| title | An interactive deep-learning workflow for head and neck gross tumour volume segmentation |
| title_full | An interactive deep-learning workflow for head and neck gross tumour volume segmentation |
| title_fullStr | An interactive deep-learning workflow for head and neck gross tumour volume segmentation |
| title_full_unstemmed | An interactive deep-learning workflow for head and neck gross tumour volume segmentation |
| title_short | An interactive deep-learning workflow for head and neck gross tumour volume segmentation |
| title_sort | interactive deep learning workflow for head and neck gross tumour volume segmentation |
| topic | Interactive deep-learning Head and neck cancer Observer study |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625001253 |
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