# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
## 2 Materials and Methods  
### Experimental Setup  
To test DL based methods for prostate segmentation, we used prostate studies of patients who underwent MRI examinations using the Achieva scanner (Philips Healthcare, Best, The Netherlands) with a pelvic phased-array coil (8 channel HD Torso XL). Specifically, from September 2019 to May 2020, 202 consecutive patients were referred to our Radiology Department to perform a prostate MRI examination. We excluded patients from the study for (a) incomplete MRI examination due to intolerance, discomfort, or claustrophobia (n = 11); (b) patients with radical prostatectomy (n = 18), subjected to transurethral resection of the prostate (TURP) (n = 20), or radiotherapy (n = 17); (c) lack of median lobe enlargement defined as intra-vesical prostate protrusion characterized by overgrowth of the prostatic median lobe into the bladder for at least 1 cm (n = 51). So, our final study population consisted of 85 patients (age range 43-75 years, mean age 59 \(\pm\) 8.4 years) with median lobe enlargement. By reviewing radiological reports, no pathological MRI findings were found in 35 patients (except for median lobe enlargement), while 50 prostate lesions (42 in peripheral zone and 8 in transitional zone) suspected for prostate cancer classified using the Prostate imaging reporting and data system (PI-RADS) 2.1 [46] were found: 18 PI-RADS 3 score, 28 PI-RADS 4 score, and 4 PI-RADS 5 score lesions with size ranged between 0.6 and 1.9 cm (mean 1.052 \(\pm\) 0.28). In addition, in our study population, by evaluating capsular involvement, 18 patients had capsular abutment and 3 patients had capsular irregularity. It means that the presence of suspected prostate cancer lesions, in our study population, can at least distend the gland boundaries. Consequently, the determination of prostate volume using the above mentioned ellipsoid formula [38] is not suitable, while manual and automatic segmentations are not (or less) affected by this issue.  
In this study, axial T2-weighted images with parameters shown in Table 1 were used. However, due to MRI protocol routine update during the study time, datasets had different resolution (2 studies with a matrix resolution of 720 \(\times\) 720; 45 studies with a matrix resolution of 672 \(\times\) 672; 23 studies with a matrix resolution of 576 \(\times\) 576; 15 studies with a matrix resolution of 320 \(\times\) 320). Consequently, the datasets had different resolutions and sizes. Since DL networks require inputs of the same size for the training process, MRI images were resampled to the isotropic voxel size of 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\) with a matrix resolution of 512 \(\times\) 512 (matrix resolution in the middle between 720 and 320) using linear interpolation. A set of trained clinical experts (FV, MP, GC, and GS authors) hand segmented the prostate region. The simultaneous ground truth estimation STAPLE tool [47] was used to combine the different segmentations from the clinical experts in a consolidated reference. Finally, manual delineations were resampled using nearest neighbor interpolation and converted to masks with 0 for the background and 1 for the prostate area.