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The use of hepatic-like cloned cord lining epithelial cells (CLEC) to enhance liver regeneration has been proposed, but has not been properly investigated in a large animal study. The paper presents a system developed for the longitudinal in-vivo volumetry study on porcine liver regeneration from computed tomography (CT) data. In this system, a rough 3D liver volume is firstly automatically segmented by a 3D mesh deformation-based method. Then a refinement step to eliminate the segmentation error is carried out by a 3D post-editing tool, followed by mesh-volume conversion and volume calculation. This system was applied in a pilot study, which was composed of 4/4 pigs in the Experimental/Control Groups, to measure liver volumes over pre- to post-operative time course. Experimental results suggest that (1) the developed system can perform CT-based porcine liver volumetry efficiently, and (2) the infusion of CLEC to liver remnant may potentially enhance the liver regeneration.

Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the posterior abdominal wall extraction using the shape model and other abdominal context, particularly with the rib-spine bone information and the wall image features. The shape model is constructed based on the training abdominal walls that are delineated manually. After bone information being extracted, the wall shape deforms from the prior shape model using the snake, which is constrained by the bone context and guided by the processed image energy map with the aim of removing distracted image features of anterior abdominal wall and the outer region from the original map. Meanwhile, an overall convex shape is maintained by limiting the angles of the contour points. The proposed approach is tested on abdominal CT data which provides encouraging results.

This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

Purpose: Characterization of focal liver lesions with various imaging modalities can be very challenging in the clinical practice and is experience-dependent. The authors' aim is to develop an automatic method to facilitate the characterization of focal liver lesions (FLLs) using multiphase computed tomography (CT) images by radiologists., Methods: A multiphase-image retrieval system is proposed to retrieve a preconstructed database of FLLs with confirmed diagnoses, which can assist radiologists' decision-making in FLL characterization. It first localizes the FLL on multiphase CT scans using a hybrid generative-discriminative FLL detection method and a nonrigid B-spline registration method. Then, it extracts the multiphase density and texture features to numerically represent the FLL. Next, it compares the query FLL with the model FLLs in the database in terms of the feature and measures their similarities using the L1-norm based similarity scores. The model FLLs are ranked by similarities and the top results are finally provided to the users for their evidence studies., Results: The system was tested on a database of 69 four-phase contrast-enhanced CT scans, consisting of six classes of liver lesions, and evaluated in terms of the precision-recall curve and the Bull's Eye Percentage Score (BEP). It obtained a BEP score of 78%. Compared with any single-phase based representation, the multiphase-based representation increased the BEP scores of the system, from 63%-65% to 78%. In a pilot study, two radiologists performed characterization of FLLs without and with the knowledge of the top five retrieved results. The results were evaluated in terms of the diagnostic accuracy, the receiver operating characteristic (ROC) curve and the mean diagnostic confidence. One radiologist's accuracy improved from 75% to 92%, the area under ROC curves (AUC) from 0.85 to 0.95 (p = 0.081), and the mean diagnostic confidence from 4.6 to 7.3 (p = 0.039). The second radiologist's accuracy did not change, at 75%, with AUC increasing from 0.72 to 0.75 (p = 0.709), and the mean confidence from 4.5 to 4.9 (p = 0.607)., Conclusions: Multiphase CT images can be used in content-based image retrieval for FLL's categorization and result in good performance in comparison with single-phase CT images. The proposed method has the potential to improve the radiologists' diagnostic accuracy and confidence by providing visually similar lesions with confirmed diagnoses for their interpretation of clinical studies.

An improved support vector machine (SVM) framework has been developed to segment hepatic tumor from CT data. By this method, the one-class SVM (OSVM) and two-class SVM (TSVM) are connected seamlessly by a boosting tool, to tackle the tumor segmentation via both offline and online learning. An initial tumor region was first pre-segmented by an OSVM classifier. Then the boosting tool was employed to automatically generate the negative (non-tumor) samples, according to certain criteria. The pre-segmented initial tumor region and the non-tumor samples generated were used to train a TSVM) classifier. By the trained TSVM classifier, the final tumor lesion was segmented out. Tested on 16 sets of CT abdominal scans, quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than the OSVM and TSVM classifiers.

A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement, and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on 10 clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76 × 0.76 × 2.0mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98%.