# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
## 4 Discussion  
In this paper, we investigate the prostatic region segmentation in MRI studies using three different DL networks (namely UNet, ENet, and ERFNet). The aim is to reduce patient mortality being only a part of prostate cancer that is clinically significant. An accurate and  
Figure 5: Comparison of 3D segmentation of prostate (patients #7, #74, and #84) for each column using the three Net architectures. The manual segmentation (yellow), ENet (red), ERFNet (blue), and U-Net (green) are superimposed. operator-independent segmentation process is needed to obtain a relevant texture-based prediction model. So, the aim of this work was not only just to test the segmentation results of the proposed models, but to evaluate if these models can yield a practical benefit in obtaining accurate and reproducible results. The inclusion of DL models in radiomics analyses will be reserved for a forthcoming paper. The first model considered in this study was UNet, which has been adopted in several image delineation processes [45]. ENet [43], and ERFNet [44] have been implemented for the segmentation process in self-driving cars, and successfully used in lung and aorta segmentation tasks [48; 49]. Specifically, they were used for the segmentation of HRCT images characterized by a very high number of slices for each study (about 600 and 450 slices for the lung and aorta studies, respectively). Authors used 32 patients' studies for the parenchyma extraction process [49], and 72 studies for the aorta segmentation process [48]. In this study, only 85 studies were used considering that each patients' image dataset consists of about 40 slices. In addition, to our knowledge, these DL models have never been applied to prostate segmentation before. In general, a DL approach requires a multitude of labeled data for training and validation purposes. For this reason, DL models are not widely used in clinical practice. As already reported in the Introduction section, there is a need to develop DL networks capable of obtaining accurate segmentations with few training examples. This issue is addressed in some studies, that is, the one-shot learning approach [53], which eliminates the need for iterative sample selection and annotation and the contrastive learning method [29] for the automated diagnosis of COVID-19 with few samples for training. In our study, we applied all three DL models to a small dataset of 85 studies provided with manual prostate segmentation adopting (i) a data augmentation strategy to reduce overfitting, (ii) a data standardization and normalization to prevent too large weights, to make the model converge faster, and to avoid numerical instability, (iii) the five-fold cross-validation strategy to obtain good results despite the few training examples, and (iv) the Tversky loss function [50] to avoid to predict most voxels as belonging to the background class. In the last case, starting from the consideration that DL methods suffer from the imbalanced data problem because the target (i.e., the prostate) is very small compared to the background, we provided a larger weight to target voxels to learn the foreground object representation more effectively. Finally, we compared the obtained performances showing that accurate and clinically acceptable prostate segmentations with few training examples were obtained using indifferently the three DL models (DSC > 87%). Specifically, results showed that ENet and UNet had better performance in minimizing the difference between automated and manual segmentations than ERFNet. Substantially, ENet and UNet were statistically identical but computationally different; ENet was much faster than UNet (see Figure 3). Also, the training Tversky loss of the UNet was much less compared to ENet. For these reasons, though UNet and ENet were not statistically different, ENet seems to be the best solution.