Domain adaptation for segmentation of critical structures for prostate cancer therapy
| Autor: | Alireza Mehrtash, Adam S. Kibel, Marko Rak, Christian Hansen, Alireza Ziaei, Clare M. Tempany, Bjoern J Langbein, Junichi Tokuda, Anneke Meyer, Oleksii Bashkanov |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Male
Wilcoxon signed-rank test Computer science Science Article 030218 nuclear medicine & medical imaging Task (project management) Domain (software engineering) 03 medical and health sciences 0302 clinical medicine Text mining Urethra Humans Segmentation Network model Multidisciplinary Artificial neural network business.industry Prostate Prostatic Neoplasms Pattern recognition Medicine Neural Networks Computer Artificial intelligence Tomography X-Ray Computed Transfer of learning business 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-021-90294-4 |
| Popis: | Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ($${p} p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink