3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement.LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]).The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.

Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range dependencies have shown promising performance in liver segmentation with the cost of high model complexity. Therefore, a new network architecture named G-UNETR++ is proposed to improve accuracy in liver segmentation with moderate model complexity. Two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as inputs are proposed to learn the 3D geometric features such as the boundaries between different organs and tissues. In addition, a hybrid loss function that combines dice loss, cross-entropy loss, and Hausdorff distance loss is designed to address class imbalance and improve segmentation performance in challenging cases. The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation.

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Liver segmentation is the fundamental step in computer-aided liver disease diagnosis and surgery planning. In this study, we developed a fully automatic liver extraction scheme based on an adaptive fast marching method (FMM). Firstly, a thresholding operation was applied to remove the ribs, spines and kidneys. Followed by a smooth filter for noise reduction. Secondly, a nonlinear gray scale converter was used to enhance the contrast of the liver parenchyma. The enhanced image is then eroded with 3-voxel radius so that small regions are deleted. The seed points located in the liver were selected automatically. Finally, using the processed image as a speed function, FMM was employed to generate the liver contour. Clinical validation has performed on 30 abdominal computed tomography (CT) datasets. The proposed algorithm achieved an overall true positive rate (TPR) of 0.98. It takes about 0.30 s for a 512×512-pixel slice. The method has been applied successfully for fast and accurate liver segmentation.

Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization

Active Shape Models (ASM) have been applied to various segmentation tasks in medical imaging, most successfully in 2D segmentation of objects that have a fairly consistent shape. However, several difficulties arise when extending 2D ASM to 3D: (1) difficulty in 3D labeling, (2) the requirement of a large number of training samples, (3) the challenging problem of landmark correspondence in 3D, (4) inefficient initialization and optimization in 3D. This paper addresses the 3D segmentation problem by using a small number of effective 2D statistical models called oriented ASM (OASM). We demonstrate that a small number of 2D OASM models, which are derived from a chunk of a contiguous set of slices, are sufficient to capture the shape variation between slices and individual objects. Each model can be matched rapidly to a new slice by using the OASM algorithm¹. Our experiments in segmenting breast and bone of the foot in MR images indicate the following: (1) The accuracy of segmentation via our method is much better than that of 2DASM-based segmentation methods.² (2) Far fewer landmarks are required compared with thousands of landmarks needed in true 3D ASM. Therefore, far fewer training samples are required to capture details. (3) Our method is computationally slightly more expensive than the 2D method2 owing to its 2 level dynamic programming (2LDP) algorithm.

Cross-sectional imaging based on navigation and virtual reality planning tools are well-established in the surgical routine in orthopedic surgery and neurosurgery. In various procedures, they have achieved a significant clinical relevance and efficacy and have enhanced the discipline's resection capabilities. In abdominal surgery, however, these tools have gained little attraction so far. Even with the advantage of fast and high resolution cross-sectional liver and pancreas imaging, it remains unclear whether 3D planning and interactive planning tools might increase precision and safety of liver and pancreas surgery. The inability to simply transfer the methodology from orthopedic or neurosurgery is mainly a result of intraoperative organ movements and shifting and corresponding technical difficulties in the on-line applicability of presurgical cross sectional imaging data. For the interactive planning of liver surgery, three systems partly exist in daily routine: HepaVision2 (MeVis GmbH, Bremen), LiverLive (Navidez Ltd, Slovenia) and OrgaNicer (German Cancer Research Center, Heidelberg). All these systems have realized a half- or full-automatic liver-segmentation procedure to visualize liver segments, vessel trees, resected volumes or critical residual organ volumes, either for preoperative planning or intraoperative visualization. Acquisition of data is mainly based on computed tomography. Three-dimensional navigation for intraoperative surgical guidance with ultrasound is part of the clinical testing. There are only few reports about the transfer of the visualization of the pancreas, probably caused by the difficulties with the segmentation routine due to inflammation or organ-exceeding tumor growth. With this paper, we like to evaluate and demonstrate the present status of software planning tools and pathways for future pre- and intraoperative resection planning in liver and pancreas surgery.

Efficacy of long-term treatment with lurasidone in children and adolescents with bipolar depression: Interim analysis of a 2-year open-label study

In this work we propose an active surface method to segment complete liver volumes from preoperative CT abdominal images. The method finds the surface that minimizes an energy function combining intensity inside and outside the surface, gradient information and curvature restrictions. The implementation is based on a level set technique following a multi-resolution strategy to reduce computing time. It requires only a single seed point inside the liver to initialize the active surface. The algorithm has been validated on a set of previously diagnosed livers. Resulting segmentations have been supervised by clinicians and radiologists, and numerically evaluated in terms of volume measurements with respect to those obtained from radiologists' manual segmentations. Additionally, radiologists analyzed the necessity of additional corrections on segmenting volumes.

Liver segmentation in CT images is a complex and challenging process. This is due to wide variability of liver sizes and shapes from one image to another, in addition to the inhomogeneity of the gray-level within the liver region and the low contrast to the background levels. In this paper, a fully automatic approach for liver segmentation is introduced. The approach consists of three main stages; pre-processing, segmentation and post processing. Watershed segmentation algorithm is used in the main processing stage to detect the borders and edges accurately between the liver regions and the background. However, because of the over-segmentation caused by the watershed algorithm, region merging algorithm is applied in the post processing stage. The merging criteria were proposed to maximize the disparity between the liver regions and the background and in the same time to keep the variance of the gray-level in the liver regions under certain threshold. The algorithm achieved 91% overall accuracy when evaluated using CT images from the MICCAI dataset.

In this paper, we present an improved active shape model (ASM) for facial feature extraction. The original ASM method developed by Cootes et al. highly relies on the initialization and the representation of the local structure of the facial features in the image. We use color information to improve the ASM approach for facial feature extraction. The color information is used to localize the centers of the mouth and the eyes to assist the initialization step. Moreover, we model the local structure of the feature points in the RGB color space. Besides, we use 2D affine transformation to align facial features that are perturbed by head pose variations. In fact, the 2D affine transformation compensates for the effects of both head pose variations and the projection of 3D data to 2D. Experiments on a face database of 50 subjects show that our approach outperforms the standard ASM and is successful in facial feature extraction

The three-dimensional (3D) liver and tumor segmentation of liver computed tomography (CT) has very important clinical value for assisting doctors in diagnosis and prognosis. This paper proposes a tumor 3D conditional generation confrontation segmentation network (T3scGAN) based on conditional generation confrontation network (cGAN), and at the same time, a coarse-to-fine 3D automatic segmentation framework is used to accurately segment liver and tumor area. This paper uses 130 cases in the 2017 Liver and Tumor Segmentation Challenge (LiTS) public data set to train, verify and test the T3scGAN model. Finally, the average Dice coefficients of the validation set and test set segmented in the 3D liver regions were 0.963 and 0.961, respectively, while the average Dice coefficients of the validation set and test set segmented in the 3D tumor regions were 0.819 and 0.796, respectively. Experimental results show that the proposed T3scGAN model can effectively segment the 3D liver and its tumor regions, so it can better assist doctors in the accurate diagnosis and treatment of liver cancer.

CoProLITE: Constrained Proxy Learning for lIver and hepaTic lesion sEgmentation

Liver segmentation is important for the computer-assisted diagnosis of liver disease and liver volume measurement. Because of the complexity of liver computed tomography (CT) images, multiple image segmentation techniques are required to robustly segment the liver. Many liver segmentation approaches have been proposed in the past few decades, however the liver segmentation problem remains. More robust approaches are needed and new possibilities should be investigated. In this paper, we propose a novel 3D liver segmentation approach that contains four major parts: (1) image pre-processing, (2) edge-constrained liver segmentation by 3D region growing, (3) separation of 3D liver and other organs, and (4) post-processing. In the first step, an anisotropic diffusion filter removes noise effectively while keeping the image edges almost unchanged. In the second step, both 3D and 2D canny edges are combined to constrain the region growing process. The seeds of region growing are generated by slope difference distribution threshold selection of the 3D CT image and morphological erosion. In the third step, 3D random walk and slope difference distribution threshold selection are combined to separate the adhered organs. In the fourth step, a novel 2D vascular region correction method is proposed for post-processing. Two open accessible CT liver datasets are used to evaluate the proposed approach and the achieved average DICE measure accuracy is 96.83%.

BackgroundThree-dimensional (3D) visualization is thought to improve the anatomical understanding of clinicians, thus improving patient safety.Case presentationA 58-year-old male was admitted to our hospital in April 2007 with a suspected metastasis of a sigmoid cancer in the Couinaud segment (CS) 7. The anatomical situation of this patient was analyzed using both a CT scan and 3D images. The initial CT scan revealed that the proximal part of the middle hepatic vein was completely missing and the metastasis in the CS 7 was closely attached to the right hepatic vein. After analyzing additional 3D images, it became clear that due to the close proximity of metastasis and right hepatic vein, the resection of the right hepatic vein was inevitable. Based on this 3D analysis, it was decided to perform a right-sided hemihepatectomy. In this case report, 3D visualization resulted in a faster and clearer understanding of the unique anatomical situation in a patient with complicated liver anatomy than transversal CT images.ConclusionThe here presented data shows for the first time 3D visualization of intravenous anastomoses in the human liver. The information offered by 3D visualization is not redundant but rather serves as a true source of additional information, indicating the potential benefit of 3D visualization in surgical operation planning.