A Deep Learning Approach for Liver Segmentation and Lesion Detection in Medical Images Using U-Net Segmentation Model

Abstract

Liver segmentation and lesion detection in biomedical applications has gained noticeable attention due to the labor-intensive efforts required to manually segment regions of interest (e.g., liver, tumor) from CT scans for efficient and accurate diagnosis. Segmentation in this context has been a comprehensive area of development to assess liver disease, enabling physicians to evaluate liver volume, morphology, and the presence of lesions. Recent advancements in biomedical imaging and deep learning approaches have led to various efforts for automated segmentation at higher level of confidence proposing significant advantages with respect to efficiency and accuracy. Accordingly, an approach for automated liver segmentation and lesion detection was developed in this study utilizing a deep learning-based U-Net model. A novel approach to automatically segment the liver and detect hepatic lesions in medical images was proposed using a U-Net model, a popular deep learning architecture for biomedical image segmentation tasks. Model training and validation was performed based on a small image set of 20 IRCAD tomography bases, compiled from the IRCAD repository, hosted by the Institute for Research Against Digestive Cancer. Various preprocessing steps on each image slice were first performed (e.g., resizing, normalization) to handle potential model over/under-fitting and enhance its robustness against dynamic image modalities. Model performance with respect to the six selected performance metrics (i.e., Dice coefficient, sensitivity, specificity, Jaccard index, precision, F1-score) demonstrated excellent model performance of >95% accuracy when tested on a subset of purely unseen images from the test set. The analysis showed that such automated segmentation via deep learning can be achieved for customized datasets while providing significant advantages over traditional labor-intensive manual segmentation approaches.