Automatic Detection and Segmentation of Liver Tumors in Multi- phase CT Images by Phase Attention Mask R-CNN
Ryô Hasegawa, Yutaro Iwamoto, Xian‐Hua Han, Lanfen Lin, Hongjie Hu, Xiujun Cai, Yen‐Wei Chen
Abstract
In computer-aided diagnosis of liver tumors, tumor detection and segmentation are essential pretreatment steps. In this study, we proposed a phase attention mask R-CNN based method for simultaneous detection and segmentation of liver tumors in multi-phase CT images. Each feature of the triple phase image is selectively extracted by the attention network for each scale. The segmentation accuracy (Dice value) is about 0.60 ~ 0.66 for single-phase CT images, and the accuracy can be improved to about 0.77 by the proposed method using attention network with multi-phase CT images.
Topics & Concepts
Artificial intelligenceSegmentationComputer scienceFeature (linguistics)Pattern recognition (psychology)Phase (matter)Computer visionImage segmentationComputer-aided diagnosisImage (mathematics)PhysicsLinguisticsQuantum mechanicsPhilosophyAdvanced Neural Network ApplicationsRadiomics and Machine Learning in Medical ImagingAI in cancer detection