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.

The Liver Tumor Segmentation Benchmark (LiTS)

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1118/1.3013555

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.

Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud–based evaluation framework is presented in this paper including results of benchmarking current state–of–the–art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non–manually–annotated medical images are available to the research community.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks

Discriminative dictionary learning for abdominal multi-organ segmentation.

Purpose We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation. Methods The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patientspecific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results. Results The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%. Conclusions This study verified the effectiveness of twostage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabi

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography.

This paper presents an effective combination of a statistical atlasbased approach and a graph cuts algorithm for fully automated robust and accurate segmentation. Major contribution of this paper is proposal of two new submodular energies for graph cuts. One is shape constrained energy derived from a statistical atlas based segmentation and the other is for constraint from a neighbouring structure. The effectiveness of the proposed energies was demonstrated using a synthesis image with different errors in shape estimation and clinical CT volumes of liver and lung.

Automated segmentation of 3D CT images based on statistical atlas and graph cuts

Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However,
precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape,
internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a
non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False
extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are
available then the overall outcome of segmentation can be improved by subtracting two phase images, and the
connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map,
tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical
over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment,
40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver
region is effective and robust despite the presence of hepatic tumors within the liver.

/pdf/segmentation-of-liver-region-with-tumorous-tissues-31o20uk79h.pdf

Segmentation of liver region with tumorous tissues

Primary malignant liver tumor, including hepatocellular carcinoma (HCC), caused 1.25 million deaths per year worldwide. Multiphase CT images offer clinicians important information about hepatic cancer. The presence of HCC is indicated by high-intensity regions in arterial phase images and low-intensity regions in equilibrium phase images following enhancement with contrast material. We propose an automatic method for detecting HCC based on edge detection and subtraction processing. Within a liver area segmented according to our scheme, black regions are selected by subtracting the equilibrium phase images to the corresponding registrated arterial phase images. From these black regions, the HCC candidates are extracted as the areas without edges by using Sobel and LoG edge detection filters. The false-positive (FP) candidates are eliminated by using six features extracted from the cancer and liver regions. Other FPs are further eliminated by opening processing. Finally, an expansion process is applied to acquire the 3D shape of the HCC. The cases used in this experiment were from the CT images of 44 patients, which included 44 HCCs. We extracted 97.7% (43/44) HCCs successfully by our proposed method, with an average number of 2.1 FPs per case. The result demonstrates that our edge-detection-based method is effective in locating the cancer region by using the information obtained from different phase images.

/pdf/computer-aided-detection-cad-of-hepatocellular-carcinoma-on-3z4gv8rcaf.pdf

Computer-aided detection (CAD) of hepatocellular carcinoma on multiphase CT images

This paper describes a medical image processing competition held annually in Japan from 2002 to 2008. The purpose of the competition is to evaluate existing segmentation algorithms and boost this type of research in Japan. The targets of segmentation are a liver and a pancreas in a contrast-enhanced multiphase computed tomography volume. Several algorithms are pitted against each other in the competition and visually assessed by radiologists to determine their ranking.

Kenji Shinozaki

Papers

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography.

Automated segmentation of 3D CT images based on statistical atlas and graph cuts

Segmentation of liver region with tumorous tissues

Computer-aided detection (CAD) of hepatocellular carcinoma on multiphase CT images

Medical Image Processing Competition in Japan