High-resolution microendoscopy based on the fiber bundle has been showing immense potential to early detection of precancerous and cancerous lesions in gastrointestinal epithelium, especially for low-resource areas in China. However, obtaining clinical benefit from microendoscopic diagnosis usually remains in the hands of experts. Quantitative analysis focusing on computer-aided detection is therefore receiving attention as an attractive tool. In this paper, we present an automatic quantification method of crypts in gastrointestinal epithelium for high-resolution microendoscopic images, which is composed of four modules: filtering, contrast enhancement, crypt segmentation and morphologic quantification of crypts. The preliminary experiments on ex vivo image data indicate that the proposed method is effective for crypt segmentation from microendoscopic images with low-contrast, and quantitation of well-defined clinical features, which has a potential in future computer-aided diagnostic systems by revealing the morphologic characteristics of crypts at various clinical stages. The proposed method also enables instant processing. Thus, it may be a powerful tool for assisting endoscopists in real-time interpretation of high-resolution microendoscopic images, with high accuracy and consistent diagnosis. Furthermore, we are testing the method on larger gastrointestinal epithelium images and in vivo high-resolution microendoscopic images, and will integrate this work into a computer-aided diagnostic system.
Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.
Liver segmentation is a basic and indispensable function in systems of computer aided liver surgery for volume calculation, operation designing and risk evaluation. Traditional manual segmentation is very time consuming because of the complicated contours of liver and the big amount of images. For increasing the efficiency of the clinical work, in this paper, a fully-automatic method was proposed to segment the liver from multi-phase contrast-enhanced computed tomography (CT) images. As an advanced region growing method, we applied various pre- and post-processing to get better segmentation from the different phases. Fifteen sets of clinical abdomens CT images of five patients were segmented by our algorithm, and the results were acceptable and evaluated by an experienced surgeon. The running-time is about 30 seconds for a single-phase data which includes more than 200 slices.
It is of vital importance that providing detailed and accurate information about hepatic vein (HV) for liver surgery
planning, such as pre-operative planning of living donor liver transplantation (LDLT). Due to the different blood flow
rate of intra-hepatic vascular systems and the restrictions of CT scan, it is common that HV and hepatic portal vein (HPV) are both filled with contrast medium during the scan and in high intensity in the hepatic venous phase images. As a result, the HV segmentation result obtained from the hepatic venous phase images is always contaminated by HPV which makes accurate HV modeling difficult. In this paper, we proposed a method for quick and accurate HV extraction. Based on the topological structure of intra-hepatic vessels, we analyzed the anatomical features of HV and HPV. According to the analysis, three conditions were presented to identify the nodes that connect HV with HPV in the topological structure, and thus to distinguish HV from HPV. The method costs less than one minute to extract HV and provides a correct and detailed HV model even with variations in vessels. Evaluated by two experienced radiologists, the accuracy of the HV model obtained from our method is over 97%. In the following work, we will extend our work to a comprehensive clinical evaluation and apply this method to actual LDLT surgical planning.
In this paper, we propose an improved fuzzy c-means (FCM) algorithm based on cluster height information to deal with
the sensitivity of unbalanced sized clusters in FCM. As we know, cluster size sensitivity is an major drawback of FCM,
which tends to balance the cluster sizes during iteration, so the center of smaller cluster might be drawn to the adjacent
larger one, which will lead to bad classification. To overcome this problem, the cluster height information is considered
and introduced to the distance function to adjust the conventional Euclidean distance, thus to control the effect on
classification from cluster size difference. Experimental results demonstrate that our algorithm can obtain good
clustering results in spite of great size difference, while traditional FCM cannot work well in such case. The improved
FCM has shown its potential for extracting small clusters, especially in medical image segmentation.
Liver tumor, one of the most wide-spread diseases, has a very high mortality in China. To improve success rates of liver
surgeries and life qualities of such patients, we implement an interactive liver surgery planning system based on contrastenhanced
liver CT images. The system consists of five modules: pre-processing, segmentation, modeling, quantitative
analysis and surgery simulation. The Graph Cuts method is utilized to automatically segment the liver based on an
anatomical prior knowledge that liver is the biggest organ and has almost homogeneous gray value. The system supports
users to build patient-specific liver segment and sub-segment models using interactive portal vein branch labeling, and to
perform anatomical resection simulation. It also provides several tools to simulate atypical resection, including resection
plane, sphere and curved surface. To match actual surgery resections well and simulate the process flexibly, we extend
our work to develop a virtual scalpel model and simulate the scalpel movement in the hepatic tissue using multi-plane
continuous resection. In addition, the quantitative analysis module makes it possible to assess the risk of a liver surgery.
The preliminary results show that the system has the potential to offer an accurate 3D delineation of the liver anatomy, as
well as the tumors' location in relation to vessels, and to facilitate liver resection surgeries. Furthermore, we are testing
the system in a full-scale clinical trial.