Application of deep learning to segment pelvis bones

The proper identification and localisation of pelvic bone metastases begins with precise segmentation of the pelvic bones. Existing pelvic bone segmentation algorithms are generally manual or semi-automatic, and they exhibit low accuracy when dealing with picture appearance changes caused by multi-site domain shifts, etc. This paper presents a strategy for segmenting normal pelvic bone characteristics in multiparametric magnetic resonance imaging (mpMRI) using a 3D U-Net based on deep learning. Extensive testing on our dataset indicates the usefulness of our automated technique. The 3D U-Net network, based on deep learning, offers patients accurate identification and segmentation of pelvic bone metastases.