Title: Application of deep learning to segment pelvis bones

Authors: N. Thamaraikannan; M. Saravanan; N.K. Anushkannan; S. Ramesh; C. Sivakumaran

Addresses: Department of Computer Science Engineering, Saveetha Engineering College, Chennai, India ' Department of ECE, Sri Eshwar College of Engineering, Kondampatti [Post], Vadasithur, Coimbatore, India ' Department of ECE, Kathir College of Engineering, Coimbatore, India ' Department of Chemistry, R.M.D. Engineering College, Kavaraipettai, 601206, India ' Photon Technologies, Chennai, 600017, India

Abstract: 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.

Keywords: U-Net; pelvis bone; segmentation; biomedical signal processing.

DOI: 10.1504/IJMEI.2025.147586

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.4, pp.382 - 392

Received: 23 May 2022
Accepted: 10 Sep 2022
Published online: 24 Jul 2025 *

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