Segmentation of liver tumors in multiphase computed tomography images using hybrid method

The multiphase scan has enabled an improved detection of liver tumors. However, tumor regions and peripheral tissues are difficult to distinguish and delineate owing to their highly similar image features. Moreover, their characteristics vary significantly in different phases. This is challenging when using segmentation methods that are based on unique training models. Herein, a hybrid framework is proposed for liver tumor segmentation in multiphase images. We first develop a cascade region-based convolutional neural network with refined head to locate the tumors. Meanwhile, phase-sensitive noise filtering is introduced to refine the segmentation conducted by a level-set-based framework. This method is sensitive to the intensity contrast but not to the regions of interest, thereby affording better performance in delineating adjacent tumors. In our experiment, the average precision and recall rates are 76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false positive rate are 72.7%, 76.2%, and 4.75%, respectively.