Bone age assessment segmentation is a medical imaging task that involves the automated or semi-automated process of identifying and delineating regions of interest (ROI) in X-ray images related to bone age assessment. Bone age assessment is a technique used in pediatric medicine to determine a child's skeletal maturity by comparing their X-ray images with standard reference images of known age.
Pre-processing: The acquired X-ray images may undergo pre-processing to enhance the image quality, reduce noise, and improve the segmentation process.
ROI Identification: In this step, the algorithm aims to identify the regions of interest (ROI) in the X-ray image that contain the bones and growth plates relevant for assessing bone age.
Segmentation: The segmentation process involves separating the identified ROIs from the rest of the image. This can be done using various computer vision and image processing techniques, such as thresholding, edge detection, region-growing, or deep learning-based methods.
Feature Extraction: After segmentation, relevant features are extracted from the segmented ROIs. These features could include measurements of the bones, growth plates, and other bone-related characteristics.
Bone Age Assessment: Once the relevant features are extracted, they are compared with established reference data to estimate the child's bone age. Radiologists or medical professionals often use standardized bone age atlases or reference charts to make this assessment.
Automating the segmentation process can be helpful to reduce subjectivity and improve the accuracy and efficiency of bone age assessment. Deep learning techniques, such as convolutional neural networks (CNNs), have shown promise in automating the segmentation of bones and growth plates in X-ray images for bone age assessment.
