Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation

It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning

PURPOSE

For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment.

METHOD

The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments.

RESULTS

The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy.

CONCLUSION

Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.

A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method

Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.

Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT

Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.

Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition

The segmentation of lungs with severe pathology is a nontrivial problem in the clinical application. Due to complex structures, pathological changes, individual differences, and low image quality, accurate lung segmentation in clinical 3-D computed tomography (CT) images is still a challenging task. To overcome these problems, a novel dictionary-based approach is introduced to automatically segment pathological lungs in 3-D low-dose CT images. Sparse shape composition is integrated with the eigenvector space shape prior model, called eigenspace sparse shape composition, to reduce local shape reconstruction error caused by the weak and misleading appearance prior information. To initialize the shape model, a landmark recognition method based on discriminative appearance dictionary is introduced to handle lesions and local details. Furthermore, a new vertex search strategy based on the gradient vector flow field is also proposed to drive the shape deformation to the target boundary. The proposed algorithm is tested on 78 3-D low-dose CT images with lung tumors. Compared to the state-of-the-art methods, the proposed approach can robustly and accurately detect pathological lung surface.

Automatic Segmentation of Liver from CT Scans with CCP–TSPM Algorithm

With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver’s anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex–concave point for tree structured part model (CCP–TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP–TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method’s effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method.

Deep Sequential Segmentation of Organs in Volumetric Medical Scans

Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints-first, they require resizing the volume to the lower-resolutional reference dimensions, and second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time. We address these problems by a U-Net-like architecture consisting of bidirectional convolutional long short-term memory and convolutional, pooling, upsampling, and concatenation layers enclosed into time-distributed wrappers. Our network can either process the full volumes in a sequential manner or segment slabs of slices on demand. We demonstrate performance of our architecture on vertebrae and liver segmentation tasks in 3-D computed tomography scans.

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice-Sørensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

Hybrid method combining superpixel, random walk and active contour model for fast and accurate liver segmentation

Organ segmentation is an important pre-processing step in surgery planning and computer-aided diagnosis. In this paper, we propose a fast and accurate liver segmentation framework. Our proposed method combines a knowledge-based slice-by-slice Random Walk (RW) segmentation algorithm (proposed in our previous work) with a superpixel algorithm called the Contrast-enhanced Compact Watershed (CCWS) method to reduce computing time and memory costs. Compared to the commonly used Simple Linear Iterative Clustering (SLIC), we demonstrate that our CCWS is more appropriate for liver segmentation. To improve the methods accuracy, we use a modified narrow band active contour model as a refinement after the initial segmentation. The experiments showed that the superpixel-based slice-by-slice RW could segment the entire liver with improved speed, and the modified active contour model is more precise than the original Chan-Vese Model. As a result, the proposed framework is able to quickly and accurately segment the entire liver.

Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver's bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works.

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images

Liver segmentation is an essential procedure in computer-assisted surgery, radiotherapy, and volume measurement. It is still a challenging task to extract liver tissue from 3D CT images owing to nearby organs with similar intensities. In this paper, an automatic approach integrating multi-dimensional features into graph cut refinement is developed and validated. Multi-atlas segmentation is utilized to estimate the coarse shape of liver on the target image. The unsigned distance field based on initial shape is then calculated throughout the whole image, which aims at automatic graph construction during refinement procedure. Finally, multi-dimensional features and shape constraints are embedded into graph cut framework. The optimal liver region can be precisely detected with a minimal cost. The proposed technique is evaluated on 40 CT scans, obtained from two public databases Sliver07 and 3Dircadb1. The dataset Sliver07 is considered as the training set for parameter learning. On the dataset 3Dircadb1, the average of volume overlap is up to 94%. The experiment results indicate that the proposed method has ability to reach the desired boundary of liver and has potential value for clinical application.

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

Universal Liver Extraction Algorithm: An Improved Chan–Vese Model

Liver segmentation is important to speed up liver disease diagnosis. It is also useful for detection, recognition, and measurement of objects in liver images. Sufficient work has been carried out until now, but common methodology for segmenting liver image from CT scan, MRI scan, PET scan, etc., is not available. The proposed methodology is an effort toward developing a general algorithm to segment liver image from abdominal computerized tomography (CT) scan and magnetic resonance imaging (MRI) scan images. In the proposed algorithm, pixel intensity range of the liver portion is obtained by cropping a random section of the liver. Using its histogram, threshold values are calculated. Further, threshold-based segmentation is performed, which separates liver from abdominal CT scan image/abdominal MRI scan image. Noise in the liver image is reduced using median filter, and the quality of the image is improved by sigmoidal function. The image is then converted into binary image. The Chan–Vese (C–V) model demands an initial contour, which evolves outward. A novel algorithm is proposed to identify the initial contour inside the liver without user intervention. This initial contour propagates outward and continues until the boundary of the liver is identified accurately. This process terminates by itself when the entire boundary of the liver is detected. The method has been validated on CT images and MRI images. Results on the variety of images are compared with existing algorithms, which reveal its robustness, effectiveness, and efficiency.

A variational approach to liver segmentation using statistics from multiple sources

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of [Formula: see text], the best root mean square symmetric surface distance (RMSD) of [Formula: see text] mm, the best maximum symmetric surface distance (MSD) of [Formula: see text] mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of [Formula: see text] mm, the best RMSD of [Formula: see text] mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan

Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images.

3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

PURPOSE

A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors.

METHODS

The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence.

RESULTS

The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively.

CONCLUSIONS

The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

Adaptive local window for level set segmentation of CT and MRI liver lesions

We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).

3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection

3D Statistical Shape Models (3D-SSM) are widely used for medical image segmentation. However, during segmentation, they typically perform a very limited unidirectional search for suitable landmark positions in the image, relying on weak learners or use-case specific appearance models that solely take local image information into account. As a consequence, segmentation errors arise, and results in general depend on the accuracy of a previous model initialization. Furthermore, these methods become subject to a tedious and use-case dependent parameter tuning in order to obtain optimized results. To overcome these limitations, we propose an extension of 3D-SSM by landmark-wise random regression forests that perform an enhanced omni-directional search for landmark positions, thereby taking rich non-local image information into account. In addition, we provide a long distance model fitting based on a multi-scale approach, that allows an accurate and reproducible segmentation even from distant image positions, thus enabling an application without model initialization. Finally, translation of the proposed method to different organs is straightforward and requires no adaptation of the training process. In segmentation experiments on 45 clinical CT volumes, the proposed omni-directional search significantly increased accuracy and displayed great precision regardless of model initialization. Furthermore, for liver, spleen and kidney segmentation in a competitive multi-organ labeling challenge on publicly available data, the proposed method achieved similar or better results than the state of the art. Finally, liver segmentation results were obtained that successfully compete with specialized state-of-the-art methods from the well-known liver segmentation challenge SLIVER.

Fully automated MR liver volumetry using watershed segmentation coupled with active contouring

PURPOSE

Our purpose is to develop a fully automated scheme for liver volume measurement in abdominal MR images, without requiring any user input or interaction.

METHODS

The proposed scheme is fully automatic for liver volumetry from 3D abdominal MR images, and it consists of three main stages: preprocessing, rough liver shape generation, and liver extraction. The preprocessing stage reduced noise and enhanced the liver boundaries in 3D abdominal MR images. The rough liver shape was revealed fully automatically by using the watershed segmentation, thresholding transform, morphological operations, and statistical properties of the liver. An active contour model was applied to refine the rough liver shape to precisely obtain the liver boundaries. The liver volumes calculated by the proposed scheme were compared to the "gold standard" references which were estimated by an expert abdominal radiologist.

RESULTS

The liver volumes computed by using our developed scheme excellently agreed (Intra-class correlation coefficient was 0.94) with the "gold standard" manual volumes by the radiologist in the evaluation with 27 cases from multiple medical centers. The running time was 8.4 min per case on average.

CONCLUSIONS

We developed a fully automated liver volumetry scheme in MR, which does not require any interaction by users. It was evaluated with cases from multiple medical centers. The liver volumetry performance of our developed system was comparable to that of the gold standard manual volumetry, and it saved radiologists' time for manual liver volumetry of 24.7 min per case.

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

PURPOSE

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.

METHODS

The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.

RESULTS

The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively.

CONCLUSIONS

The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.