## 2 Materials and Methods  
### Description of the Datasets  
One of the main strengths of this study is the use of five different prostate T2-weighted MR datasets. As shown in Table 1, there is a significant variability in scanner manufacturers, resolutions and magnetic field strengths, among other factors. Datasets \"Girona\" [20], \"Promise12\" [21] and \"Prostate-3T\" [22] are all freely available for download on the Internet, while \"IVO\" comes from the Valencian Institute of Oncology, and \"Private\" comes from a private institution which has decided to remain anonymous. Furthermore, Promise12 is an ongoing prostate segmentation challenge, wherein 50 MR prostate images are provided along with their segmentation masks (dataset \"Promise12\"), and 30 additional images are provided without segmentations as a test set (dataset \"Promise12_test\"). The participants must submit their predictions to the challenge server, where they are evaluated. Hence, \"Promise12_test\" will only be used for testing.  
In addition to that, the prostate segmentations follow varying criteria depending on the expert who segmented them. In \"IVO\" dataset, three different radiologists with two, five and seven years of experience in prostate cancer imaging took turns to perform the segmentations. In \"Private\" dataset, a single medical physicist with two years of experience in MR prostate imaging segmented all the images. In \"Promise12\", each of the four rows in Table 1 corresponds to a different medical center and, by extension, were also segmented by at least one different expert each, although an expert from the Promise12 challenge [21] corrected some of them. For the other datasets, no further information about the segmentations is known.  
Regarding exclusion criteria, before separating the images into any subsets, all segmentations were examined and those with obvious errors were directly excluded. Therefore, no corrections were made, so as to better preserve the particular criteria from each expert (except for the \"Private\" dataset, in which all segmentations were revised). The number of samples (N) in Table 1 is computed after this filtering. As a special mention, 18 images from \"Prostate-3T\", which were also present in \"Promise12\" or \"Promise12_test\" (although with different GT segmentations), were also discarded; and other 30 images from \"Prostate-3T\" (half of the original dataset), which systematically left many slices in the base and apex unsegmented, had to be discarded as well. Figure 1 shows the center slice of a sample from each of the datasets.  
For the 3D-US segmentation model, two different datasets were employed: \"IVO\" and \"Private\", both coming from the same institutions as their homonymous MR datasets. For \"IVO\" (\(N=160\) images), five differentrologists with six to thirty years of experience segmented the images, while for \"Private\" (\(N=82\) images), it was tworologists with more than ten years of experience; no exclusion criteria were applied. Images from both datasets were captured using Hitachi scanners at spacings of 0.20 mm to 0.41 mm in any axis. Figure 1 shows the center slice of a sample from each axis. Unfortunately, no further segmented datasets were found on the Internet for this image modality.