Automatic segmentation of femoral tumors by nnU-net.

Journal: Clinical biomechanics (Bristol, Avon)
PMID:

Abstract

BACKGROUND: Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element analysis of a patient's femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture and the need for a prophylactic fixation. Improving the accuracy of such analyses ruqires an automatic and accurate segmentation of the tumors and their automatic inclusion in the finite element model. We present herein a deep learning algorithm (nnU-Net) to automatically segment lytic tumors within the femur.

Authors

  • Oren Rachmil
    Computational Mechanics & Experimental Biomechanics Lab, School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Ramat Aviv 69978, Israel.
  • Moran Artzi
    Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Moshe Iluz
    Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.
  • Ido Druckmann
    Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.
  • Zohar Yosibash
    Computational Mechanics & Experimental Biomechanics Lab, School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Ramat Aviv 69978, Israel. Electronic address: yosibash@tauex.tau.ac.il.
  • Amir Sternheim
    Faculty of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel; Department of Orthopedic Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.