Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

TLDR

Extraction of bone contours from radiographs is crucial for disease diagnosis, preoperative planning, and treatment analysis. The authors propose a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. The approach generates candidate femur positions with a global detector, refines them using a statistical shape model and local point detectors, and applies Random Forest regression voting to select optimal positions, yielding robust results on 839 images. The system outperforms alternative matching techniques, achieving a mean point‑to‑curve error below 0.9 mm on 99 % of the images and represents the most accurate automatic proximal femur segmentation reported to date.

Abstract

Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.