Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

Deng Yao Tu, Peng Chan Lin, Hsin Hung Chou, Meng Ru Shen, Sun Yuan Hsieh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

Original languageEnglish
Pages (from-to)3267-3277
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
Publication statusPublished - 2023 Sept 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics


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