Size-adaptive hepatocellular carcinoma detection from 3D CT images based on the level set method

Automatic detection of hepatocellular carcinoma (HCC) from 3D CT images effectively reduces interpretation work. Several detection methods have been proposed. However, there still remains a tough problem of adaptation detection methods to a wide range of tumor sizes, especially to small nodules, since it is difficult to distinguish tumors from other structures, including noise. Although the level set method (LS) is a powerful tool for detecting objects with arbitrary topology, it is still poor at detecting small nodules due to low contrast. To detect small nodules, early phase images are useful since low contrast in the late phase causes miss-detection of some small nodules. Nevertheless, conventional methods using early phase images face two problems: one is failure to extract small nodules due to low contrast even in early phase images, and the other is false-positive (FP) detection of vessels adjacent to tumors. In this paper, a new robust detection method adapted to the wide range of tumor sizes has been proposed that uses only early phase images. To overcome these two problems, our method consists of two techniques. One is regularizing surface evolution used in LS by applying a new HCC filter that can enhance both small nodules and large tumors. The other is regularizing the surface evolution by applying a Hessian-matrix-based filter that can enhance the vessel structures. Experimental results showed that the proposed method improves sensitivity by over 15% and decreases FP by over 20%, demonstrating that the proposed method is useful for detecting HCC accurately.