Prostate Cancer Semantic Segmentation by Gleason Score Group in bi-parametric MRI with Self Attention Model on the Peripheral Zone
| Autor: | Duran, Audrey, Jodoin, Pierre-Marc, Lartizien, Carole |
|---|---|
| Přispěvatelé: | Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Videos and Images Theory and Analytics Laboratory (VITAL), Université de Sherbrooke (UdeS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Lartizien, Carole |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing [INFO.INFO-IM] Computer Science [cs]/Medical Imaging [INFO.INFO-IM]Computer Science [cs]/Medical Imaging convolutional neural network magnetic resonance imaging [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] attention model prostate cancer semantic segmentation computer-aided detection |
| Zdroj: | Medical Imaging with Deep Learning (MIDL) Medical Imaging with Deep Learning (MIDL), Jul 2020, Montreal, Canada. pp.193-204 |
| Popis: | International audience; In this work, we propose a novel end-to-end multi-class attention network to jointly perform peripheral zone (PZ) segmentation and PZ lesions detection with Gleason score (GS) group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs PZ segmentation 2) the second branch uses this zonal prior as an attention gate for the detection and grading of PZ lesions. The model was trained and validated with a 5-fold cross-validation on an heterogeneous series of 98 MRI exams acquired on two different scanners prior prostatectomy. In the free-response receiver operating characteristics (FROC) analysis for clinically significant lesions (defined as GS > 6) detection, our model achieves 75.8% ±3.4% sensitivity at 2.5 false positive per patient. Regarding the automatic GS group grading, Cohen's quadratic weighted kappa coefficient is 0.35 ±0.05, which is considered as a fair agreement and an improvement with regards to the baseline U-Net model. Our method achieves good performance without requiring any prior manual region delineation in clinical practice. We show that the addition of the attention mechanism improves the CAD performance in comparison to the baseline model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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