Liver Tumor Segmentation Research Articles

Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing

Automatic segmentation of liver tumorous regions often fails due to high noise and large variance of tumors. In this work, a semi-automatic algorithm is proposed to segment liver tumors from computed tomography (CT) images. To cope with the variance of tumors, their intensity probability density functions (PDF) are modeled as a bag of Gaussians unlike the previous works where the tumor is modeled as a single Gaussian, and employ a three-dimensional seeded region growing (SRG) method. The bag of Gaussians are initialized at manually selected seeds and updated during growing process iteratively. There are two criteria to be fulfilled for growing: one is the Bayesian decision rule, and the other is a model matching measure. Once the growing is terminated, morphological operations are performed to refine the result. This method, showing promising performance, has been evaluated using ten CT scans of livers with twenty tumors provided by the organizer of the 3D Liver Tumor Segmentation Challenge 2008.

A semi-automated method for liver tumor segmentation based on 2D region growing with

Liver tumour segmentation from computed tomography (CT) scans is a challenging task. A semi-automatic method based on 2D region growing with knowledge-based constraints is proposed to segment lesions from constituent 2D slices obtained from 3D CT images. Minimal user involvement is required to define an approximate region of interest around the suspected legion area. The seed point and feature vectors are then calculated and voxels are labeled using a region-growing approach. Knowledge-based constraints are incorporated into the method to ensure the size and shape of the segmented region is within acceptable parameters. The individual segmented lesions can then be stacked together to generate a 3D volume. The proposed method was tested on a training set of 10 tumours and a testing set of 10 tumours. To evaluate the results quantitatively, various measures were used to generate scores. Based on the results obtained from the 10 testing tumours, the method was resulted in an average score of 64.

An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors

Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide. A precise analysis of the lesions would help in the staging of the tumor and in the evaluation of the possible applicable therapies. In this paper we present the workflow we have developed for the semi-automatic segmentation of liver tumors in the datasets provided for the MICCAI Liver Tumor Segmentation contest. Since we wanted to develop a system that could be as automatic as possible and to follow the segmentation process in every single step starting from the image loading to the lesion extraction, we decided to subdivide the workflow in two main steps: first we focus on the segmentation of the liver and once we have extracted the organ structure we segment the lesions applying an adaptive multi-thresholding system.

Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning

A semi-automatic scheme was developed for the segmentation of 3D liver tumors from computed tomography (CT) images. First a support vector machine (SVM) classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumor-containing slices were processed. The method was tested using 3D CT images with 10 liver tumors and a set of quantitative measures were computed, resulted in an averaged overall performance score of 72.

Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach

In this paper, we present a fully automated system that detects and segments potential liver cancer tumors from a thin slice CT data. The system is targeted toward a tumor whose volume is larger than 1 cm^3 1cm3, and is efficient as the average computation time per volume in our experiment is roughly 3.5 minutes.The system first reduces the volume size by 4x4x4 to reduce the computation and memory requirements. It then detects candidate locations as local minima of the intensity fields after a variant of textit{elastic quadratic} smoothing. It then provides a rough segmentation at each candidate by fitting a plane at sampled points near the periphery of the concave region in the intensity profiles. The rough segmentation is used to estimate the range of intensity values in the tumor, which is used to obtain a more accurate segmentation by a method originally developed for pulmonary nodules. The result of the second segmentation is interpolated at the resolution of the original data.The development of the system is a part of the 2008 3D Segmentation in the Clinic: A Grand Challenge competition. Four CT volumes containing 10 tumors were used for the development of the algorithm. Additional six CT volumes containing 10 tumors were used to test the segmentation performance.

Cognition Network Technology for a Fully Automated 3D Segmentation of Liver Tumors

The Definiens Cognition Network Technology is applied to detect automatically tumors in a human liver. On the basis of a test data set containing ten tumors we show first quantitative results which are compared to manual segmentations provided by medical experts.

Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing

This article presents an algorithm for the segmentation of liver metastases in CT scans. It is a hybrid method that combines adaptive thresholding based on a gray value analysis of the ROI with model-based morphological processing. We show the results of the MICCAI liver tumor segmentation competition 2008 which were successful for all ten tumors.

Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume

This paper describes an ensemble segmentation trained by the AdaBoost algorithm, which finds a sequence of weak hypotheses, each of which is appropriate for the distribution on training example, and combines the weak hypotheses by a weighted majority vote. In our study, a weak hypothesis corresponds to a weak segmentation process. This paper shows a procedure for generating an ensemble segmentation algorithm using AdaBoost, and applies it to a liver lesion extraction problem from a contrast enhanced abdominal CT volume. A leave-one-patient-out validation test using 16 CT volumes demonstrated the effectiveness of the generated ensemble segmentation algorithm. In addition, we evaluated the performance by applying the algorithm to unknown test data provided by the �3D Liver Tumor Segmentation Challenge 2008�.

An iterative Bayesian approach for liver analysis: tumors validation study

We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and tumors from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. Only one user-defined voxel seed for the liver and additional seeds according to the number of tumors inside the liver are required for initialization. The algorithm do not require manual adjustment of internal parameters. In this work, a retrospective study on a validated clinical dataset totaling 20 tumors from 9 patients CTAs� was performed. An aggregated competition score of 61 was obtained on the test set of this database. In addition we measured the robustness of our algorithm to different seeds initializations. These results suggest that our method is clinically applicable, accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-automatic segmentation methods.

Interactive Liver Tumor Segmentation Using Graph-cuts and Watershed

We present in this paper an application of minimal surfaces and Markov random fields to the segmentation of liver tumors. The originality of the work consists in applying these models to the region adjacency graph of a watershed transform. We detail the assumptions and the approximations introduced in these models by using a region graph instead of a pixel graph. This strategy leads to an interactive method that we use to delineate tumors in 3D CT images. We detail our strategy to achieve relevant segmentations of these structures and compare our results to hand made segmentations done by experienced radiologists. This paper summarizes our participation to the MICCAI 2008 workshop called: “3D segmentation in the clinic : A Grand Challenge II”.

A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans

Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 x 512-pixel slice). A volume overlap of 94.2% and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6% and 87.5%, respectively.

Focal nodular hyperplasia in biliary atresia patient after Kasai hepatic portoenterostomy

We present the case of a 10-year-old girl with biliary atresia in whom focal nodular hyperplasia (FNH) of the liver occurred following successful "Kasai" hepatic portoenterostomy at the age of 2 months. Her jaundice completely disappeared a few weeks after the operation. A 4-cm diameter liver tumor in segment IV was found when she was 5-years old. Needle biopsy could not establish a definitive diagnosis. Because the tumor size was not changing, she was conservatively followed by abdominal echo at an outpatient clinic. She showed progressive liver dysfunction and ongoing cirrhosis, and so at the age of 10 years, she received living donor liver transplantation at our institute. The tumor was pathologically diagnosed as a FNH. Though a FNH in biliary atresia patients is extremely rare, there are only two cases reported in the literature; it should be considered in the differential diagnosis for a hepatic nodule during a long follow-up course in patients with biliary atresia.

A case of mucin producing liver metastases with intrabiliary extension

A 75-year-old man was admitted to our hospital with a diagnosis of liver metastases from colon cancer. He underwent right hemicolectomy for cecal cancer eight years ago, and had a metastatic liver tumor in segment 8 (S8), which was surgically resected about 4 years after the initial operation. Histopathological examination of the resected specimens from both operations revealed a well-differentiated adenocarcinoma with mucinous carcinoma. Four months after the second operation, computed tomography demonstrated a low-density lesion at the cut surface of the remnant liver. Although it was considered to be a postoperative collection of inflammatory fluid, it formed a cystic configuration and increased in size to approximately 5 cm in diameter. With a tentative diagnosis of a recurrence of metastatic cancer, partial hepatectomy of S8 was performed. Histological examination of the resected specimens also revealed mucinous adenocarcinoma, which had invaded into the biliary ducts, replacing and extending along its epithelium. Immunohistochemically, the tumor cells were positive for cytokeratin (CK) 20, but negative for CK7. Therefore, the tumor was diagnosed as a metastatic adenocarcinoma from colonic cancer. Liver metastases of colorectal adenocarcinoma sometimes invade the Glisson's triad and grow along the biliary ducts.

Segmentation of liver tumors using the Insight Segmentation and Registration Toolkit

Segmentation of liver tumors using the Insight Segmentation and Registration Toolkit