An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms

AUTOMATIC LIVER SEGMENTATION FROM CT SCANS USING INTENSITY ANALYSIS AND LEVEL-SET ACTIVE CONTOURS

Liver segmentation from CT scans is still a challenging task due to the liver characteristics in terms of shape and intensity variability. In this work, we propose an automatic segmentation method of the liver from CT data sets. The framework consists of three main steps: liver shape model localization, liver intensity range estimation and localized active contouring. We proposed an adaptive multiple thresholding technique to estimate the range of the liver intensities. First, multiple thresholding is used to extract the dense tissue from the whole CT scan. A localization step is then used to find the approximate location of the liver in the CT scan, to localize a constructed mean liver shape model. A liver intensity-range estimation step is then applied within the localized shape model ROI. The localized shape model and the estimated liver intensity range are used to build the initial mask. A level set based active contour algorithm is used to deform the initial mask to the liver boundaries in the CT scan. The proposed method was evaluated on two public data sets: SLIVER07 and 3D-IRCAD. The experiments showed that the proposed method is able to segment to liver in all CT scans in the two data sets accurately

3D Segmentation of Soft Tissues by Flipping-free Mesh Deformation

Ph.DDOCTOR OF PHILOSOPH

Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Liver plays an important role in metabolic processes, therefore fast diagnosis and potential surgical plan- ning is essential in case of any disease. The automatic liver segmentation approach has been studied dur- ing the past years and different segmentation techniques have been proposed, but this task remains a challenge and improvements are still required to further increase segmentation accuracy. In this work, an automatic, deep learning based approach is introduced, which is adaptable and it is able to handle smaller databases, including heterogeneous data. The method starts with a preprocessing to highlight the liver area using probability density function based estimation and supervoxel segmentation. Then, a modification of the 3D U-Net is introduced, which is called 3D RP-UNet and applies the ResPath in the 3D network. Finally, with liver-heart separation and morphological steps, the segmentation results are further refined. Segmentation results on three public databases showed that the proposed method performs robustly and achieves good segmentation performance compared to other state-of-the-art approaches in the majority of the evaluation metrics

Автоматическое определение плотности печени по данным компьютерной томографии и ультранизкодозной компьютерной томографии

РЕНТГЕНОЛОГИЧЕСКИЕ ИССЛЕДОВАНИЯТОМОГРАФИЯ РЕНТГЕНОВСКАЯ КОМПЬЮТЕРНАЯ /ИСПТОМОГРАФИЯ ТРАНСМИССИОННАЯ КОМПЬЮТЕРНАЯ /ИСПТОМОДЕНСИТОМЕТРИЯ /ИСПУЛЬТРАНИЗКОДОЗНАЯ КОМПЬЮТЕРНАЯ ТОМОГРАФИЯ /ИСППЕЧЕНЬ /РЕНТГЕНОГРСТЕАТОЗ ПЕЧЕНИ /ДИАГННЕАЛКОГОЛЬНАЯ ЖИРОВАЯ БОЛЕЗНЬ ПЕЧЕНИ /ДИАГНПЛОТНОСТЬ ПЕЧЕНИРЕТРОСПЕКТИВНЫЕ ИССЛЕДОВАНИЯЦель. Оценить возможности разработанного метода автоматического определения плотности печени по данным нативны хультранизкодозных и стандартных компьютерных томограмм органов грудной клетки, при которых в область сканирования попадает верхний этаж брюшной полости. Материал и методы. Проведен ретроспективный анализ данных 10000 пациентов, которым была выполнена ультранизкодозная компьютерная томография. Из них отобраны 100 пациентов, дополнительно прошедших стандартную компьютерную томографию. Средний возраст пациентов: 62,5±12 лет (M±сигма). Ручное измерение плотности печени проводилось во II, IV, VII-VIII сегментах. Дополнительно измерялась плотность селезенки. Стеатоз печени считался достоверным при плотности печени <40 HU, при отношения плотностей печени и селезенки <0,8-1,0, при разнице их плотностей<10 HU. Для автоматического измерения была разработана программа определения плотности печени, включающая сегментацию и определение плотности сегментированной области. Результаты. При сравнении показателей плотности печени для стандартной компьютерной томографии, полученных автоматическим и ручным методами, выявлена незначительная разница (51,43 и 50,37 HU, p=0,0192). Для ультранизкодозной компьютерной томографии различие несколько больше (54,90 и 55,60 HU, p=0,310). При оценке разницы сравниваемых методов для стандартной и ультранизкодозной компьютерной томографии существенных различий не выявлено (р=0,0035). Автоматический метод относительно ручного выявляет большее количество случаев пониженной плотности печени для стандартной компьютерной томографии (10 и 6 случаев, P(McNemar)=0.125) и для ультранизкодозной (11 и 5 случаев, P(McNemar)=0.0313). Согласие между двумя методами удовлетворительное для обоих протоколов сканирования (kappa 0,726 и 0,593). Заключение. Хорошая корреляция ручного и автоматического методов для стандартной и ультранизкодозной компьютерной томографии позволяет использовать автоматический метод для анализа большого объема данных и выявления стеатоза печени.Objective. To evaluate the possibilities of the developed method for the automatic liver density measurements according to the data of native ultra-low-dose and standard chest computed tomograms in the case when an upper segment of the abdomen is in the scanned zone. Methods. Retrospective analysis of clinical data associated with patients (n=10,000) underwent ultra-low-dose computed tomography has been performed. The patients (n=100) were selected and additionally underwent standard computed tomography. The average age of patients was 62.5±12 years (M±sigma). Manual measurement of the liver density was carried out in II, IV, VII-VIII segments. In addition the splenic density was measured. In the case of the liver density was <40 HU, liver-to-spleen ratio (L/S) <0.8-1.0, and the density difference was <10 HU hepatic steatosis was considered to be reliable. For automatic procedure a program for measurement of liver density including segmentation and the segmented density area was developed. Results. A little difference was revealed in comparison of the automated and manual liver density measurement for standard computed tomography (51.43 vs. 50,37 HU, p=0.0192). For ultra-low-dose computed tomography the difference is slightly larger (54.90 and 55.60 HU, p=0.310). When assessing the difference between the compared methods for standard and ultra-low-dose computed tomography, no significant difference was found (p=0.0035). In comparison of manual and automated methods a larger number of the low liver density cases both for standard (10 vs. 6 cases, P(McNemar)=0.125) and ultra-low-dose tomograms (11 vs. 5 cases, P(McNemar)=0.0313) was detected. The agreement between two methods is considered to be satisfactory for both scanning protocols (kappa 0.726 vs. 0.593). Conclusion. A good correlation between manual and automated methods for standard and ultra-low-dose computed tomography allows using the automatic method for analyzing a large amount of data and revealing the hepatic steatosis

Liver segmentation using 3D CT scans.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2018.Abstract available in PDF file

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term