Automated Fractured Femur Segmentation using CNN

Fracture fixation surgeries require a careful and well thought out surgical plan, mainly due to the wide range of possibilities in the fracture types and available choices in fixation constructs. There is considerable interest in virtual 3D planning tools ranging from 3D visualization, interactive fracture reduction and bio-mechanical analysis of fracture fixation construct stability to arrive at optimal plan. One of the key steps prior to reconstructing 3D fractures is accurate fracture segmentation which can be tedious and time consuming even with semi-automated tools. In this paper, we report preliminary results from our attempt to fully automate the segmentation of fractured bone using deep learning. We performed experiments using the widely used 3D segmentation model called 3D U-Net on a dataset of 14 CT volumes. The dataset is randomly divided into train, validation and test splits comprising 7, 3 and 4 volumes respectively. Even with a small training set of femur fractures, we were able to achieve a mean dice score of 0.861 with a mean sensitivity of 0.899. The model was able to capture the challenging fracture regions and could cleanly separate the femur head and socket. Apart from this, we also studied the impact of different loss functions on the network’s performance. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation. Further improvement is expected with a larger collection of such fractured samples.


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Work Title Automated Fractured Femur Segmentation using CNN
Open Access
  1. Jared Vicory
  2. Pranjal Sahu
  3. Hwabok Wee
  4. Hannah Nam
  5. Avani Chopra
  6. J. Spence Reid
  7. Spence Reid
  8. Gregory Lewis
  9. Venkata S. Arikatla
  10. Sreekanth Arikatla
License In Copyright (Rights Reserved)
Work Type Article
Publication Date January 1, 2022
Publisher Identifier (DOI)
Deposited January 30, 2023




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Work History

Version 1

  • Created
  • Added 120322Z.pdf
  • Added Creator Jared Vicory
  • Added Creator Pranjal Sahu
  • Added Creator Hwabok Wee
  • Added Creator Hannah Nam
  • Added Creator Avani Chopra
  • Added Creator J. Spence Reid
  • Added Creator Spence Reid
  • Added Creator Gregory Lewis
  • Added Creator Venkata S. Arikatla
  • Added Creator Sreekanth Arikatla
  • Published