A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation

Abstract

Liver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual differentiation. Manual identification of tumors in the liver is time-consuming, intricate, and susceptible to errors with potential repercussions for patient care. While machine learning–based approaches have emerged for liver organ recognition and segmenting the tumor, they continue to face challenges related to recognition accuracy and the inability to distinguish tumors of varied sizes. To solve the problems, a multiattention network made up of cascaded ResUNet and U-Net with attention mechanisms was proposed in this study. We investigated liver tumor segmentation with various configurations of U-Net, ResUNet, U-Net with attention mechanisms, and ResUNet with attention mechanisms on augmented and nonaugmented data. We used the 3Dircadb dataset for training and validation purposes, and the proposed method was evaluated on dice score, intersection of union (IoU), recall, and precision. The performance metrics achieved with this method on the dataset are as follows: approximately 0.89 for the dice coefficient, 0.90 for IoU, 0.93 for recall, and 0.96 for precision in the case of liver segmentation without data augmentation and 0.92, 0.90, 0.92, and 0.94, respectively, for dice score, IoU, recall, and precision with data augmentation. For tumor segmentation, the metrics include 0.70 for dice coefficient, 0.61 for IoU, 0.91 for recall, and 0.94 for precision when the data were augmented but 0.83 for dice score, 0.78 for IoU, and 0.89 and 0.90, respectively, for recall and precision.