ArticleBMC medical imaging2026
Automated morphometric segmentation analysis of hand X-ray image using deep learning network.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe rapid development of deep learning in computer vision has led to increasing interest in its applications to medical image analysis. While many studies have focused on morphometric measurements from MR and CT images, comparatively few works have applied deep learning to extract morphometric features from hand X-ray images.
objectivesThis research aims to establish a scheme for deep learning based automatic segmentation and morphology feature extraction of hand X-ray images with accuracy and efficiency.
methodsA total of 668 hand X-ray images from patients were obtained and were randomly classified as 498 training images(74.6%), 102 validation images(15.2%) and 68 testing images(10.2%). A combination of nnU-net and YOLO deep learning model is applied to perform the automatic segmentation. The outcomes were benchmarked against manual segmentations performed by experienced clinicians, enabling an assessment of the model's accuracy and overall performance. An in-house program was applied to the segmented part to extract the morphology features, such as Finger Bone Length(FBL) and Finger Bone Width(FBW).
resultsFor nnU-net training, the IoU score on the training dataset is 0.9596 and the IoU score on the validation dataset is 0.9584. For YOLOv8 training, we got an accuracy of 0.9816 and a loss of 0.0627. For feature extraction, the Pearson and Spearmann coefficient between automatic and manual results along with ICC shows the consistency of our automatic feature extraction method and human extracted method at a 95% confidence level, and the mean difference is close to zero. As a result, our scheme proved both accurate and precise.
conclusionsWe present a two-stage, clinically interpretable workflow for hand X-ray analysis that combines nnU-Net-based hand ROI segmentation with YOLO bone-wise instance segmentation, followed by automated extraction of finger-bone morphometric indicators (FBL/FBW). Beyond reporting segmentation/detection metrics, we quantitatively validate the extracted morphometrics against expert measurements using correlation, ICC, and Bland-Altman analysis, demonstrating consistent bone-wise agreement and supporting the potential utility of the pipeline for standardized clinical measurements.
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