Automatic detection and segmentation of prostate cancer using deep learning techniques

Abstract

Prostate cancer is a major cause of death among men worldwide, and detecting it usually involves invasive procedures. Magnetic resonance imaging (MRI) has become a common research area for detecting this cancer because it represents a less invasive option. However, segmenting the prostate gland from MRI images can be a complicated task that requires expert opinions, which is both time-consuming and inconsistent. This thesis proposes a novel deep-learning architecture to automate and obtain accurate and reliable segmentation of the prostate gland in MRI scans. Precise segmentation is crucial for radiotherapy planning, as it determines the tumor’s location and size, which affects treat- ment effectiveness and reduces radiation exposure to surrounding healthy tissues. Therefore, a thorough comparison between architectures from the state-of-the-art is also performed. Convolutional neural networks have shown great potential in medical image segmenta- tion, but the uncertainty associated with their predictions is often overlooked. Therefore, this work proposes a novel approach incorporating uncertainty quantification to ensure reliable and trustworthy results. The models were evaluated on a dataset of prostate T2-MRI scans obtained in collab- oration with the Centre Hospitalarie Dijon and Universitat Politecnica de Catalunya. The results showed that the proposed architecture FAU-Net outperforms most existing models in the literature, with an improvement of 5% in the Dice Similarity Coefficient (DSC) and In- tersection over Union (IoU). However, the best model overall was R2U-Net, which achieved segmentation accuracy and uncertainty estimation values of 85% and 76% for DSC and IoU, respectively, with an uncertainty score lower than 0.05. In addition to the proposed model and comparison between models for prostate seg- mentation and uncertainty quantification, a web application was presented for easier access to the trained models in a clinical setting. This web app would allow medical professionals to upload MRI scans of prostate cancer patients and obtain accurate and reliable segmentation quickly and easily. This would reduce the time and effort required for manual segmentation and improve patient outcomes by facilitating better treatment planning. Overall, this work presents a novel strategy for prostate segmentation using deep learn- ing models and uncertainty quantification. The proposed method provides a reliable and trust- worthy segmentation while quantifying the uncertainty associated with the predictions. This research can benefit prostate cancer patients by improving treatment planning and outcomes.