Area extraction of the liver and hepatocellular carcinoma in CT scans

Abstract

In Korea, hepatocellular carcinoma is the third frequent cause of cancer death, occupying 17.2% among the whole deaths from cancer, and the rate of death from hepatocellular carcinoma comes to about 21 out of 100,000. This paper proposes an automatic method for the extraction of areas being suspicious as hepatocellular carcinoma from computed tomography (CT) scans and evaluates the availability as an auxiliary tool for the diagnosis of hepatocellular carcinoma. For detecting tumors in the internal of the liver from a CT scan, first, an area of the liver is extracted from about 45-50 CT slices obtained by scanning in 2.5-mm intervals starting from the lower part of the chest. In the extraction of an area of the liver, after the unconcerned areas outside of the bony thorax are removed, areas of the internal organs are segmented by using information on the intensity distribution of each organ, and an area of the liver is extracted among the segmented areas by using information on the position and morphology of the liver. Because hepatocellular carcinoma is a hypervascular tumor, the area corresponding to hepatocellular carcinoma appears more brightly than the surroundings in a CT scan, and also takes a spherical shape if the tumor shows expansile growth pattern. By using these features, areas being brighter than the surroundings and globe-shaped are segmented as candidate areas for hepatocellular carcinoma in the area of the liver, and then, areas appearing at the same position in successive CT slices among the candidates are discriminated as hepatocellular carcinoma. For the performance evaluation of the proposed method, experimental results obtained by applying the proposed method to CT scans were compared with the diagnoses by radiologists. The evaluation results showed that all areas of the liver and hypervascular tumors were extracted exactly and the proposed method has a high availability as an auxiliary diagnosis tool for the discrimination of liver tumors.

Fig. 1

a CT image with obscure boundaries of the liver. b CT image with the liver being smaller than other internal organs

Fig. 2

Example of underestimated tumor candidates in the method using 3-dimensional multiphase multislice CT images

Fig. 3

Overall procedure for the extraction of an area of the liver

Fig. 4

Extraction of an area of internal body using an area of the bony thorax

Fig. 5

Quantization and pseudocolorization of an area of internal body

Fig. 6

Area segmentation by levels of pseudocolors in a pseudocolorization CT slice

Fig. 7

Area mergence for the liver

Fig. 8

Example of results of area mergence and classification

Fig. 9

Area of the liver containing the vena cava

Fig. 10

Degree of overlap between areas of the liver extracted from two successive CT slices

Fig. 11

A hypervascular tumor in an area of the liver

Fig. 12

Candidate areas of hypervascular tumors segmented from an area of the liver

Fig. 13

Extraction procedure of areas of hypervascular tumors using successive CT slices

Fig 14

Areas of the liver and hypervascular tumors extracted from a CT scan.

Fig. 15

Extraction of areas of hypervascular tumors from two additional CT scans