Kenji Shinozaki

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

PurposeWe propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation.MethodsThe algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results.ResultsThe effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%.ConclusionsThis study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.

Automated segmentation of 3D CT images based on statistical atlas and graph cuts

This paper presents an effective combination of a statistical atlasbased approach and a graph cuts algorithm for fully automated robust and accurate segmentation. Major contribution of this paper is proposal of two new submodular energies for graph cuts. One is shape constrained energy derived from a statistical atlas based segmentation and the other is for constraint from a neighbouring structure. The effectiveness of the proposed energies was demonstrated using a synthesis image with different errors in shape estimation and clinical CT volumes of liver and lung.

Segmentation of liver region with tumorous tissues

Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape, internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are available then the overall outcome of segmentation can be improved by subtracting two phase images, and the connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map, tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment, 40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver region is effective and robust despite the presence of hepatic tumors within the liver.

Computer-aided detection (CAD) of hepatocellular carcinoma on multiphase CT images

Primary malignant liver tumor, including hepatocellular carcinoma (HCC), caused 1.25 million deaths per year worldwide. Multiphase CT images offer clinicians important information about hepatic cancer. The presence of HCC is indicated by high-intensity regions in arterial phase images and low-intensity regions in equilibrium phase images following enhancement with contrast material. We propose an automatic method for detecting HCC based on edge detection and subtraction processing. Within a liver area segmented according to our scheme, black regions are selected by subtracting the equilibrium phase images to the corresponding registrated arterial phase images. From these black regions, the HCC candidates are extracted as the areas without edges by using Sobel and LoG edge detection filters. The false-positive (FP) candidates are eliminated by using six features extracted from the cancer and liver regions. Other FPs are further eliminated by opening processing. Finally, an expansion process is applied to acquire the 3D shape of the HCC. The cases used in this experiment were from the CT images of 44 patients, which included 44 HCCs. We extracted 97.7% (43/44) HCCs successfully by our proposed method, with an average number of 2.1 FPs per case. The result demonstrates that our edge-detection-based method is effective in locating the cancer region by using the information obtained from different phase images.

Medical Image Processing Competition in Japan

This paper describes a medical image processing competition held annually in Japan from 2002 to 2008. The purpose of the competition is to evaluate existing segmentation algorithms and boost this type of research in Japan. The targets of segmentation are a liver and a pancreas in a contrast-enhanced multiphase computed tomography volume. Several algorithms are pitted against each other in the competition and visually assessed by radiologists to determine their ranking.