Automated femur segmentation from computed tomography images using a deep neural network

TL;DR

This paper introduces a deep learning-based automated method for segmenting the proximal femur in CT images, significantly improving speed and accuracy to aid osteoporosis screening and fracture risk assessment.

Contribution

The study presents a novel deep convolutional neural network approach, based on U-net architecture, for automatic femur segmentation from CT scans, reducing manual effort and errors.

Findings

Achieved a mean Dice similarity coefficient of 0.990

Attained a 95th percentile Hausdorff distance of 0.981 mm

Demonstrated robustness and speed in femur segmentation

Abstract

Osteoporosis is a common bone disease that occurs when the creation of new bone does not keep up with the loss of old bone, resulting in increased fracture risk. Adults over the age of 50 are especially at risk and see their quality of life diminished because of limited mobility, which can lead to isolation and depression. We are developing a robust screening method capable of identifying individuals predisposed to hip fracture to address this clinical challenge. The method uses finite element analysis and relies on segmented computed tomography (CT) images of the hip. Presently, the segmentation of the proximal femur requires manual input, which is a tedious task, prone to human error, and severely limits the practicality of the method in a clinical context. Here we present a novel approach for segmenting the proximal femur that uses a deep convolutional neural network to produce accurate, automated, robust, and fast segmentations of the femur from CT scans. The network architecture is based on the renowned u-net, which consists of a downsampling path to extract increasingly complex features of the input patch and an upsampling path to convert the acquired low resolution image into a high resolution one. Skipped connections allow us to recover critical spatial information lost during downsampling. The model was trained on 30 manually segmented CT images and was evaluated on 200 ground truth manual segmentations. Our method delivers a mean Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) of 0.990 and 0.981 mm, respectively.