Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Abstract

Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

“…For instance, dilated convolutional U-Net, which involves multiple dilated convolutions following a standard convolution, was employed in a modified U-Net with recurrent nodes in order to preserve contextual information and spatial resolution ( 36 ). Some models employed combinations of transformer models and modified U-Nets, allowing for preservation of contextual features such as edge enhancement ( 45 , 49 ). Cascaded 3D U-Net likewise employ two U-Net architectures in series, with the first trained on down-sampled images and the second trained on full-resolution images, allowing for a combination of granularity and refinement of the features of choice ( 39 ).…”

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

“…However, to accurately detect malignant tumors during pathological biopsy requires at least 50 histological slides to represent a large three-dimensional plane of the tumor [13]. This means that pathologists spend a lot of time preparing specimens and processing histological images; furthermore, with a large amount of data available, physicians are prone to miss and misdiagnosis, and there has been a significant increase in medical error lawsuits resulting from misdiagnosis since the 1980s [14][15][16][17]. This has led to a sharp rise in healthcare costs.…”

An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings

“…While these innovations have been employed in certain medically progressive areas and have addressed numerous issues [17,18], applying them to the healthcare systems of developing countries presents challenges due to the following reasons:…”