Attention-Guided Mask R-CNN for Liver Tumor Segmentation and Classification from CT Scans: A Tailored Approach
TL;DR
An attention-controlled automated liver tumor segmentation and classification based on a customized Mask Region Convolutional Neural Network with an addition of Multi-Scale Attention Gate (MSAG) is introduced with affirm the usefulness and clinical appropriateness of the suggested framework.
Abstract
Computed tomography (CT) images have poor tissue contrasts, irregular appearance of lesions, and unclear tumor borders which limit reliable diagnosis of liver malignancy. This paper introduces an attention-controlled automated liver tumor segmentation and classification based on a customized Mask Region Convolutional Neural Network (tm-RCNN) with an addition of Multi-Scale Attention Gate (MSAG). To increase the local contrast and reduce artifacts caused by acquisition, adaptive histogram equalization is used. The suggested MSAG selectively elevates boundary-sensitive features in a variety of spatial resolutions, which allow accurate delineation of lesions within the tm-RCNN decoder. Deep residual features, geometric form descriptors and enhanced median binary pattern (e-MBP) textures are extracted using segmented areas and then classified using a hybrid SqueezeNet DeepMaxout ensemble with score-level fusion. It has been experimentally validated on two benchmarking CT sets with a higher performance that achieves a Dice coefficient of 0.9587, classification accuracy of 0.936, sensitivity of 0.961 and less computational time of 64.21 s than the state-of-the-art. These findings affirm the usefulness and clinical appropriateness of the suggested framework.