# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging  
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1 RlMED Foundation, Via Bandeira, 11, 90133 Palermo, Italy; [EMAIL_ADDRESS]  
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2 Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalu, Italy 2  
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2 Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalu, Italy 2  
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4 Dipartimento di Bhomocfica, Neuroscienze e Diagrosta svanzata (BIND), University of Palermo, 90127 Palermo, Italy; [EMAIL_ADDRESS] (F.V. ); [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (G.C. ); [EMAIL_ADDRESS] (A.B. ); [EMAIL_ADDRESS] (G.S.) [PERSON]  
3 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; [EMAIL_ADDRESS] (N.D.); anthony. types of segmentation techniques have been developed, for example, [7, 8, 9]. Some of the previous techniques include thresholding [10], k-means clustering [11], watersheds [12], followed by more advanced algorithms such as active contour methods [8, 13], graph cuts [14], random walks [15], conditional and Markov random fields [16] to name a few. In recent years, particularly the last decade, the field of Machine Learning (ML) and Deep Learning (DL) has seen exponential growth and has produced models that have shown remarkable performance across many benchmark datasets and many different problem domains [17, 18]. In general, an artificial intelligence method learns from examples and makes predictions without prior specific programming [19]. In the case of DL, these models implement networked structures to mimic the human brain transforming imaging data in feature vectors. Briefly, between the input and output, a variable number of hidden layers is implemented and the various nodes are connected to others with different weights. The initial development of DL models was towards image classification problems, followed by object detection and finally, image segmentation, which is seen as a pixel level classification problem where each pixel is classified with one of many possible label classes. For example, in tumor segmentation, every voxel can be classified as either belonging to the class label of the object of interest (target) or the background. Since it is a very common task across many different problem domains, hundreds of different DL based models have been presented for the delineation task over the past several years: fully convolutional [20], encoder-decoder [21], multi-scale and pyramid [22, 23, 24], attention [25], recurrent neural [26], generative and adversarial training [27, 28] based networks. Even during the current pandemic, DL networks have been widely used to help clinicians diagnose COVID-19 [29, 30]. It is beyond the scope of this paper to discuss and describe all these different types of models. Interested readers are directed to recent comprehensive reviews [31, 32] of DL based methods/models for image segmentation. In this study, we deal with the issue of prostate region delineation on magnetic resonance imaging (MRI) studies. Prostatic volume extraction helps in the planning of biopsies, surgeries, focal ablative, radiation, and minimally invasive (e.g., intensity focused ultrasound [33]) treatments. In addition, benign prostatic hyperplasia, also called prostate enlargement, is one of the most common conditions affecting men [34]. A correlation between prostatic volume, and the incidence of prostate cancer, where early tumor identification is crucial to reduce mortality, has been shown in [35]. Since only part of prostate cancer is clinically significant, risk stratification is mandatory to avoid over-diagnosis and over-treatment [36, 37]. For this reason, radiomics in MRI has acquired a crucial role in the risk stratification process [19, 36]. MRI allows calculating prostatic volume considering the prostate as an ellipsoid. Unfortunately, the shape of the prostate varies and the determination of its volume based on the ellipsoid formula is often incorrect [38].