Tomoko Tateyama

Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm

Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast of CT images by probability density function estimation. Then, to detect tumorous regions, we use the expectation maximization/maximization of the posterior marginal (EM/MPM) algorithm, which utilizes both the intensity and label information of the adjacent regions. Finally, a shape constraint is applied to reduce noise and identify focal tumors. Quantitative evaluation experiments show that our method can accurately and effectively detect tumors even in poor-contrast CT images.

Preliminary study on statistical shape model applied to diagnosis of liver cirrhosis

In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in the shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic livers. In constructing a SSM of the liver, we first normalize volume data followed by the construction of the model using principal component analysis. The coefficients of the model are used as indicators of liver pathology. The effectiveness of the constructed model is evaluated by the classification accuracy of both normal and abnormal data.

Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctors opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases.

Simultaneous Segmentation of Multiple Organs Using Random Walks

Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p < 0.001).

Segmentation of liver and spleen based on computational anatomy models

Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs (p<0.00001).

2D-PCA Based Statistical Shape Model from few Medical Samples

Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We then apply 2D-PCA to construct a statistical shape model with generalization even from fewer samples.

Segmentation of high resolution satellite images by direction and morphological filters

This paper examines images taken from IKONOS to extract several features such as road relations automatically. We propose a new method which combines color, texture information and shape information for segmentation of high resolution satellite images. The method uses color and texture information for global segmentation, and shape information for local analysis. We propose a new direction filter which pays its attention to road features having information on specific directionality. We also propose another new morphology filter which is used as a length filter extracting length of each region more efficiently.

Computer-aided liver surgical planning system using CT volumes

In this paper, we presented our newly developed computer-aided liver surgical planning system for patient-specific treatments by using the patients CT volumes. The system is composed of three modules, liver segmentation, vessel extraction, and visualization & interaction modules. It can prepare a virtual environment for patient-specific liver surgical planning and simulations. We also developed an original visualization library, which is based on GPU (graphics processing unit) computing for real-time interaction and visualization. The effectiveness of our system was evaluated by surgeons with liver surgery simulations.

Novel Liver Visualization and Surgical Simulation System

BackgroundSuccessful liver surgery requires an understanding of the patient’s particular liver characteristics, including shape and vessel distribution. In clinical medicine, there is a high demand for surgical assistance systems to assess individual patients. Our aims in this study were to segment the liver based on computed tomography volume data and to develop surgical plans for individual patients.MethodsThe hepatic vessels were semi-automatically extracted from the segmented liver images, and the 3D shape of the liver and extracted vessel distribution were visualized using a surgical simulation system.ResultsThe 3D visualization of the liver allowed easy recognition of vessel and tumor location and selection of these structures with the 3D pointing device. The surgeon’s prior knowledge and clinical experience were integrated into the visualization system to create a practical virtual surgery, leading to improved functionality and accuracy of information recognition in the surgical simulation system.ConclusionsThe 3D visualization demonstrated details of individual liver structure, resulting in better understanding and practical surgical simulation.

Automated assessment of small bowel motility function based on three-dimensional zero-mean normalized cross correlation

In this paper, we propose an automated method for assessment of small bowel contraction movement based on a three-dimensional zero-mean normalized cross correlation method (3D-ZNCC) with cine-MRI. The correlation value between frames is proportional to area change of the small bowel and the temporal area change can be used as a measure of the small bowel contraction movement. In the conventional two-dimensional zero-mean normalized correlation (2D-ZNCC) method, only frame A and frame B are used for calculation of correlation between frame A and frame B. Since a complex shape change of the intestine is irregular, it was difficult to detect contraction movement. In order to solve this problem, we propose to use 3D ZNCC, in which sequence frames are used for correlation calculations instead of single frame. Since not only spatial changes, but also temporal changes are included, we can detect the contraction movement correctly. Experimental results show that our method is better than conventional method.