1. Deep learning for elective neck delineation: More consistent and time efficient
- Author
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Frederik Maes, S. Willems, Sandra Nuyts, J. van der Veen, and Heleen Bollen
- Subjects
Observer Variation ,medicine.medical_specialty ,business.industry ,Deep learning ,Planning target volume ,Hematology ,Time efficient ,030218 nuclear medicine & medical imaging ,Clinical Practice ,Surface distance ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Oncology ,Head and Neck Neoplasms ,030220 oncology & carcinogenesis ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Neural Networks, Computer ,Radiology ,Artificial intelligence ,business ,Observer variation ,Neoplasm Staging - Abstract
Background/purpose Delineation of the lymph node levels of the neck for irradiation of the elective clinical target volume in head and neck cancer (HNC) patients is time consuming and prone to interobserver variability (IOV), although international consensus guidelines exist. The aim of this study was to develop and validate a 3D convolutional neural network (CNN) for semi-automated delineation of all nodal neck levels, focussing on delineation accuracy, efficiency and consistency compared to manual delineation. Material/methods The CNN was trained on a clinical dataset of 69 HNC patients. For validation, 17 lymph node levels were manually delineated in 16 new patients by two observers, independently, using international consensus guidelines. Automated delineations were generated by applying the CNN and were subsequently corrected by both observers separately as needed for clinical acceptance. Both delineations were performed two weeks apart and blinded to each other. IOV was quantified using Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD). To assess automated delineation accuracy, agreement between automated and corrected delineations were evaluated using the same measures. To assess efficiency, the time taken for manual and corrected delineations were compared. In a second step, only the clinically relevant neck levels were selected and delineated, once again manually and by applying and correcting the network. Results When all lymph node levels were delineated, time taken for correcting automated delineations compared to manual delineations was significantly shorter for both observers (mean: 35 vs 52 min, p 85%). Manual corrections necessary for clinical acceptance were 1.4 mm MSD on average and were especially low ( 87%). Manual corrections necessary for clinical acceptance were 1.3 mm MSD on average. IOV was significantly smaller with automated compared to manual delineations (MSD: 0.8 mm vs 2.3 mm, p Conclusion The CNN developed for automated delineation of the elective lymph node levels in the neck in HNC was shown to be more efficient and consistent compared to manual delineation, which justifies its implementation in clinical practice.
- Published
- 2020