Efficient Hand Bone Segmentation for Medical Applications Using Refined DeepLab Model

Abstract

In the medical field, analyzing various bone structures is crucial due to the rigid nature of bones. X-ray imaging plays an essential role in medical procedures, including bone age evaluation, fracture detection, and implant creation. However, operator involvement can introduce biases and increase processing time. Automating the process could reduce processing time and enhance diagnostic accuracy by minimizing biases and operator involvement. This paper introduces the Refined DeepLab model, a lightweight encoder–decoder-based approach for multiclass segmentation of hand bones. The primary objective is to assist physicians in tasks such as bone age analysis, fracture detection, hand movement analysis, and implant design. The research objectives are organized into three phases, with this work focusing on the first phase of our objectives, which is delineating bones from tissues, studying the bone structure, and multiclass segmentation of hand bones. The model utilizes DenseNet121 as its feature extractor and Sigmoid-weighted Linear Unit (SiLU) as its activation function. Experimental findings demonstrate promising performance in hand bone multiclass segmentation, with a Mean Intersection over Union (mIoU) of 85.02% and a Dice score of 92.2%. The comprehensive analysis of the results confirms that the proposed model excels, particularly in achieving the right balance between computational efficiency and precise segmentation.