A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening

Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios, e.g., fully dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase (VP) abdominal studies, or NC-only studies. We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose. We trained HPVD on 771 DCE CT scans to detect HCCs and tested on external 164 positives and 206 controls, respectively. We compare performance against six clinical readers, including two radiologists, two hepato-pancreatico-biliary (HPB) surgeons, and two hepatologists. The area under curve (AUC) of the localization receiver operating characteristic (LROC) for NC-only, NC plus VP, and full DCE CT yielded 0.71, 0.81, 0.89 respectively. At a high sensitivity operating point of 80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measured physician performance. We also demonstrate performance improvements over more typical and less flexible non hetero-phase detectors. Thus, we demonstrate that a single deep learning algorithm can be effectively applied to diverse HCC detection clinical scenarios.

Image Co-segmentation via Multi-scale Local Shape Transfer

Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category. In real-world cases, however, common foreground objects often vary greatly in appearance, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation approach that transfers patch-level local object shapes which appear more consistent across different images. In our framework, a multi-scale patch neighbourhood system is first generated using proposal flow on arbitrary image-pair, which is further refined by Locally Linear Embedding. Based on the patch relationships, we propose an efficient algorithm to jointly segment the objects in each image while transferring their local shapes across different images. Extensive experiments demonstrate that the proposed method can robustly and effectively segment common objects from an image set. On iCoseg, MSRC and Coseg-Rep dataset, the proposed approach performs comparable or better than the state-of-thearts, while on a more challenging benchmark Fashionista dataset, our method achieves significant improvements.