Comparing multi-image and image augmentation strategies for deep learning-based prostate segmentation
During MR-Linac-based adaptive radiotherapy, multiple images are acquired per patient. These can be applied in training deep learning networks to reduce annotation efforts. This study examined the advantage of using multiple versus single images for prostate treatment segmentation. Findings indicate...
| Glavni avtor: | |
|---|---|
| Format: | Article |
| Jezik: | English |
| Izdano: |
Elsevier
2024-01-01
|
| Serija: | Physics and Imaging in Radiation Oncology |
| Teme: | |
| Online dostop: | http://www.sciencedirect.com/science/article/pii/S2405631624000216 |
| Izvleček: | During MR-Linac-based adaptive radiotherapy, multiple images are acquired per patient. These can be applied in training deep learning networks to reduce annotation efforts. This study examined the advantage of using multiple versus single images for prostate treatment segmentation. Findings indicate minimal improvement in DICE and Hausdorff 95% metrics with multiple images. Maximum difference was seen for the rectum in the low data regime, training with images from five patients. Utilizing a 2D U-net resulted in DICE values of 0.80/0.83 when including 1/5 images per patient, respectively. Including more patients in training reduced the difference. Standard augmentation methods remained more effective. |
|---|---|
| ISSN: | 2405-6316 |