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Author

Andrea Schenk

Other affiliations: Charité, University of Bremen
Bio: Andrea Schenk is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Liver transplantation & Segmentation. The author has an hindex of 24, co-authored 97 publications receiving 2493 citations. Previous affiliations of Andrea Schenk include Charité & University of Bremen.


Papers
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TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.
Abstract: In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

533 citations

Journal ArticleDOI
TL;DR: Methods for a geometrical and structural analysis of vessel systems have been evaluated in the clinical environment and have been used in more than 170 cases so far to plan interventions and transplantations.
Abstract: For liver surgical planning, the structure and morphology of the hepatic vessels and their relationship to tumors are of major interest. To achieve a fast and robust assistance with optimal quantitative and visual information, we present methods for a geometrical and structural analysis of vessel systems. Starting from the raw image data a sequence of image processing steps has to be carried out until a three-dimensional representation of the relevant anatomic and pathologic structures is generated. Based on computed tomography (CT) scans, the following steps are performed. 1) The volume data is preprocessed and the vessels are segmented. 2) The skeleton of the vessels is determined and transformed into a graph enabling a geometrical and structural shape analysis. Using this information the different intrahepatic vessel systems are identified automatically. 3) Based on the structural analysis of the branches of the portal vein, their vascular territories are approximated with different methods. These methods are compared and validated anatomically by means of corrosion casts of human livers. 4) Vessels are visualized with graphics primitives fitted to the skeleton to provide smooth visualizations without aliasing artifacts. The image analysis techniques have been evaluated in the clinical environment and have been used in more than 170 cases so far to plan interventions and transplantations.

470 citations

Book ChapterDOI
11 Oct 2000
TL;DR: A fast and accurate tool for semiautomatic segmentation of volumetric medical images based on the live wire algorithm, shape-based interpolation and a new optimization method is presented.
Abstract: We present a fast and accurate tool for semiautomatic segmentation of volumetric medical images based on the live wire algorithm, shape-based interpolation and a new optimization method.

201 citations

Journal ArticleDOI
TL;DR: A fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step with a significant reduction of false positive findings when compared with the raw neural network output.
Abstract: Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step. We describe our experiments on the LiTS challenge training data set and evaluate segmentation and detection performance. Our proposed design cascading two models working on voxel- and object-level allowed for a significant reduction of false positive findings by 85% when compared with the raw neural network output. In comparison with the human performance, our approach achieves a similar segmentation quality for detected tumors (mean Dice 0.69 vs. 0.72), but is inferior in the detection performance (recall 63% vs. 92%). Finally, we describe how we participated in the LiTS challenge and achieved state-of-the-art performance.

174 citations

Journal ArticleDOI
TL;DR: A novel image processing technique was evaluated which allows a semi-automatic volume calculation and 3D visualisation of the different liver segments and no significant differences between the presented automatic volumetry and the conventional volumets were observed.
Abstract: The aim of this study was to evaluate a software tool for non-invasive preoperative volumetric assessment of potential donors in living donated liver transplantation (LDLT). Biphasic helical CT was performed in 56 potential donors. Data sets were post-processed using a non-commercial software tool for segmentation, volumetric analysis and visualisation of liver segments. Semi-automatic definition of liver margins allowed the segmentation of parenchyma. Hepatic vessels were delineated using a region-growing algorithm with automatically determined thresholds. Volumes and shapes of liver segments were calculated automatically based on individual portal-venous branches. Results were visualised three-dimensionally and statistically compared with conventional volumetry and the intraoperative findings in 27 transplanted cases. Image processing was easy to perform within 23 min. Of the 56 potential donors, 27 were excluded from LDLT because of inappropriate liver parenchyma or vascular architecture. Two recipients were not transplanted due to poor clinical conditions. In the 27 transplanted cases, preoperatively visualised vessels were confirmed, and only one undetected accessory hepatic vein was revealed. Calculated graft volumes were 1110 +/- 180 ml for right lobes, 820 ml for the left lobe and 270 +/- 30 ml for segments II+III. The calculated volumes and intraoperatively measured graft volumes correlated significantly. No significant differences between the presented automatic volumetry and the conventional volumetry were observed. A novel image processing technique was evaluated which allows a semi-automatic volume calculation and 3D visualisation of the different liver segments.

98 citations


Cited by
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Journal ArticleDOI
TL;DR: This work proposes a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D Dense UNet 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.
Abstract: 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.

1,561 citations

Journal ArticleDOI
TL;DR: All tissues and organs were reconstructed as three-dimensional unstructured triangulated surface objects, yielding high precision images of individual features of the body, which greatly enhances the meshing flexibility and the accuracy in comparison with the traditional voxel-based representation of anatomical models.
Abstract: The objective of this study was to develop anatomically correct whole body human models of an adult male (34 years old), an adult female (26 years old) and two children (an 11-year-old girl and a six-year-old boy) for the optimized evaluation of electromagnetic exposure. These four models are referred to as the Virtual Family. They are based on high resolution magnetic resonance (MR) images of healthy volunteers. More than 80 different tissue types were distinguished during the segmentation. To improve the accuracy and the effectiveness of the segmentation, a novel semi-automated tool was used to analyze and segment the data. All tissues and organs were reconstructed as three-dimensional (3D) unstructured triangulated surface objects, yielding high precision images of individual features of the body. This greatly enhances the meshing flexibility and the accuracy with respect to thin tissue layers and small organs in comparison with the traditional voxel-based representation of anatomical models. Conformal computational techniques were also applied. The techniques and tools developed in this study can be used to more effectively develop future models and further improve the accuracy of the models for various applications. For research purposes, the four models are provided for free to the scientific community.

1,347 citations

Journal ArticleDOI
TL;DR: The Second International Consensus Conference on Laparoscopic Liver Resections (LLR) was held in Morioka, Japan, from October 4 to 6, 2014 to evaluate the current status of laparoscopic liver surgery and to provide recommendations to aid its future development.
Abstract: The use of laparoscopy for liver surgery is increasing rapidly. The Second International Consensus Conference on Laparoscopic Liver Resections (LLR) was held in Morioka, Japan, from October 4 to 6, 2014 to evaluate the current status of laparoscopic liver surgery and to provide recommendations to aid its future development. Seventeen questions were addressed. The first 7 questions focused on outcomes that reflect the benefits and risks of LLR. These questions were addressed using the Zurich-Danish consensus conference model in which the literature and expert opinion were weighed by a 9-member jury, who evaluated LLR outcomes using GRADE and a list of comparators. The jury also graded LLRs by the Balliol Classification of IDEAL. The jury concluded that MINOR LLRs had become standard practice (IDEAL 3) and that MAJOR liver resections were still innovative procedures in the exploration phase (IDEAL 2b). Continued cautious introduction of MAJOR LLRs was recommended. All of the evidence available for scrutiny was of LOW quality by GRADE, which prompted the recommendation for higher quality evaluative studies. The last 10 questions focused on technical questions and the recommendations were based on literature review and expert panel opinion. Recommendations were made regarding preoperative evaluation, bleeding controls, transection methods, anatomic approaches, and equipment. Both experts and jury recognized the need for a formal structure of education for those interested in performing major laparoscopic LLR because of the steep learning curve.

1,064 citations

Journal ArticleDOI
TL;DR: An optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations is developed.
Abstract: Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s{\hbox{-}} t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation.

716 citations