Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants
This addresses the problem of accurately measuring bone growth around implants for medical professionals, though it is incremental as it applies an existing method to a specific domain.
The study developed an AI-based algorithm using a U-Net architecture to segment bone tissue in medical images with metallic implants, achieving around 98% accuracy and estimating bone volume at about 15% of conventional techniques, which tend to overestimate.
This preliminary study focuses on the development of a medical image segmentation algorithm based on artificial intelligence for calculating bone growth in contact with metallic implants. %as a result of the problem of estimating the growth of new bone tissue due to artifacts. %the presence of various types of distortions and errors, known as artifacts. Two databases consisting of computerized microtomography images have been used throughout this work: 100 images for training and 196 images for testing. Both bone and implant tissue were manually segmented in the training data set. The type of network constructed follows the U-Net architecture, a convolutional neural network explicitly used for medical image segmentation. In terms of network accuracy, the model reached around 98\%. Once the prediction was obtained from the new data set (test set), the total number of pixels belonging to bone tissue was calculated. This volume is around 15\% of the volume estimated by conventional techniques, which are usually overestimated. This method has shown its good performance and results, although it has a wide margin for improvement, modifying various parameters of the networks or using larger databases to improve training.