Research: Pelvic bones fracture examination based on CT scans

Challenge

Pelvis injuries in trauma patients are of key interest in treatment as the fractures in the pelvic region may lead to life-long disability. In the project, we focused on an algorithm for pelvis bone classification and segmentation.

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What we did

To properly address the problem of pelvic bone classification and segmentation (sacrum, left pelvic bone, right pelvic bone, and lumbar spine) in trauma patients, we collected 103 CT scans and segmented bones in those cases. Using U-net-like architecture, we distinguish the pelvic bones and their fractures. As the fractures are mostly due to communication accidents, they are classified as complete with comminuted breakage, i.e. in CT, we can see many small bones being displaced near pelvic bones. The most challenging part lies in the correct classification of tiny bones in the close vicinity of displacements of different bones.

Results

From the dataset 21 samples were selected for the test set. The final model was equipped with dedicated postprocessing to improve its discriminating ability towards the left and right pelvic bones. This was crucial as part, because with rich augmentation in network training, some small pieces of bones might be undistinguishable for the model (loss of symmetry or ambiguous parts of ilium). To assess the model performance we used dice, recall, and precision metrics, respectively. For each metric, we got at least 0.9 an average score with almost every case having a few fractures.

Figure 1: Properly classified and segmented pelvic bones. Yellow – right pelvic bone, green – left pelvic bone, red – sacrum, blue – lumbar spine. In the picture, we can see a number of fractures in the ilium part of the pelvic bones.