Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

• Published in: Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 Vol. 12901; pp. 68 - 77
• Main Authors: Zhang, Yue, Peng, Chengtao, Peng, Liying, Huang, Huimin, Tong, Ruofeng, Lin, Lanfen, Li, Jingsong, Chen, Yen-Wei, Chen, Qingqing, Hu, Hongjie, Peng, Zhiyi
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Bi-directional feature fusion; Liver tumor segmentation; Multi-phase segmentation
• ISBN: 3030871924; 9783030871925
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-87193-2_7

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts.