期刊
MEDICAL PHYSICS
卷 48, 期 7, 页码 3721-3729出版社
WILEY
DOI: 10.1002/mp.14907
关键词
computed tomography; deep learning; orbital cellulitis; segmentation
资金
- National Institutes of Health (NIH) [R01CA237277]
- UPMC Hillman Developmental Pilot Program
- NVIDIA Corporation
The study developed and validated a deep learning algorithm to automatically detect and segment orbital abscess on head CT scans, showing promising performance in strong agreement with a human observer.
Objectives To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT). Methods We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context-aware convolutional neural network (CA-CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre-trained model for CT-based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U-Net and the CA-CNN models with and without transfer learning were trained and tested on the collected dataset using the 10-fold cross-validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations. Results The context-aware U-Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78 +/- 0.12 and 0.65 +/- 0.13, which were consistently higher than the classical U-Net or the context-aware U-Net without transfer learning (P < 0.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10 +/- 0.11 mL and 1.94 +/- 1.21 mm, respectively. The context-aware U-Net detected all orbital abscess without false positives. Conclusions The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer.
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