Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms

The Liver Tumor Segmentation Benchmark (LiTS)

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.Comment: conferenc

An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

AbstractIn this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This work significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.Bjoern Menze is supported through the DFG funding (SFB 824, subproject B12) and a Helmut-Horten-Professorship for Biomedical Informatics by the Helmut-Horten-Foundation. Florian Kofler is Supported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81. An Tang was supported by the Fonds de recherche du Québec en Santé and Fondation de l’association des radiologistes du Québec (FRQS- ARQ 34939 Clinical Research Scholarship – Junior 2 Salary Award). Hongwei Bran Li is supported by Forschungskredit (Grant NO. FK-21- 125) from University of Zurich.Peer ReviewedArticle signat per 109 autors/es: Patrick Bilic 1,a,b, Patrick Christ 1,a,b, Hongwei Bran Li 1,2,∗,b, Eugene Vorontsov 3,a,b, Avi Ben-Cohen 5,a, Georgios Kaissis 10,12,15,a, Adi Szeskin 18,a, Colin Jacobs 4,a, Gabriel Efrain Humpire Mamani 4,a, Gabriel Chartrand 26,a, Fabian Lohöfer 12,a, Julian Walter Holch 29,30,69,a, Wieland Sommer 32,a, Felix Hofmann 31,32,a, Alexandre Hostettler 36,a, Naama Lev-Cohain 38,a, Michal Drozdzal 34,a, Michal Marianne Amitai 35,a, Refael Vivanti 37,a, Jacob Sosna 38,a, Ivan Ezhov 1, Anjany Sekuboyina 1,2, Fernando Navarro 1,76,78, Florian Kofler 1,13,57,78, Johannes C. Paetzold 15,16, Suprosanna Shit 1, Xiaobin Hu 1, Jana Lipková 17, Markus Rempfler 1, Marie Piraud 57,1, Jan Kirschke 13, Benedikt Wiestler 13, Zhiheng Zhang 14, Christian Hülsemeyer 1, Marcel Beetz 1, Florian Ettlinger 1, Michela Antonelli 9, Woong Bae 73, Míriam Bellver 43, Lei Bi 61, Hao Chen 39, Grzegorz Chlebus 62,64, Erik B. Dam 72, Qi Dou 41, Chi-Wing Fu 41, Bogdan Georgescu 60, Xavier Giró-i-Nieto 45, Felix Gruen 28, Xu Han 77, Pheng-Ann Heng 41, Jürgen Hesser 48,49,50, Jan Hendrik Moltz 62, Christian Igel 72, Fabian Isensee 69,70, Paul Jäger 69,70, Fucang Jia 75, Krishna Chaitanya Kaluva 21, Mahendra Khened 21, Ildoo Kim 73, Jae-Hun Kim 53, Sungwoong Kim 73, Simon Kohl 69, Tomasz Konopczynski 49, Avinash Kori 21, Ganapathy Krishnamurthi 21, Fan Li 22, Hongchao Li 11, Junbo Li 8, Xiaomeng Li 40, John Lowengrub 66,67,68, Jun Ma 54, Klaus Maier-Hein 69,70,7, Kevis-Kokitsi Maninis 44, Hans Meine 62,65, Dorit Merhof 74, Akshay Pai 72, Mathias Perslev 72, Jens Petersen 69, Jordi Pont-Tuset 44, Jin Qi 56, Xiaojuan Qi 40, Oliver Rippel 74, Karsten Roth 47, Ignacio Sarasua 51,12, Andrea Schenk 62,63, Zengming Shen 59,60, Jordi Torres 46,43, Christian Wachinger 51,12,1, Chunliang Wang 42, Leon Weninger 74, Jianrong Wu 25, Daguang Xu 71, Xiaoping Yang 55, Simon Chun-Ho Yu 58, Yading Yuan 52, Miao Yue 20, Liping Zhang 58, Jorge Cardoso 9, Spyridon Bakas 19,23,24, Rickmer Braren 6,12,30,a, Volker Heinemann 33,a, Christopher Pal 3,a, An Tang 27,a, Samuel Kadoury 3,a, Luc Soler 36,a, Bram van Ginneken 4,a, Hayit Greenspan 5,a, Leo Joskowicz 18,a, Bjoern Menze 1,2,a // 1 Department of Informatics, Technical University of Munich, Germany; 2 Department of Quantitative Biomedicine, University of Zurich, Switzerland; 3 Ecole Polytechnique de Montréal, Canada; 4 Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; 5 Department of Biomedical Engineering, Tel-Aviv University, Israel; 6 German Cancer Consortium (DKTK), Germany; 7 Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; 8 Philips Research China, Philips China Innovation Campus, Shanghai, China; 9 School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK; 10 Institute for AI in Medicine, Technical University of Munich, Germany; 11 Department of Computer Science, Guangdong University of Foreign Studies, China; 12 Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; 13 Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany; 14 Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, China; 15 Department of Computing, Imperial College London, London, United Kingdom; 16 Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany; 17 Brigham and Women’s Hospital, Harvard Medical School, USA; 18 School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel; 19 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, PA, USA; 20 CGG Services (Singapore) Pte. Ltd., Singapore; 21 Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India; 22 Sensetime, Shanghai, China; 23 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; 24 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA; 25 Tencent Healthcare (Shenzhen) Co., Ltd, China; 26 The University of Montréal Hospital Research Centre (CRCHUM) Montréal, Québec, Canada; 27 Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montréal, Canada; 28 Institute of Control Engineering, Technische Universität Braunschweig, Germany; 29 Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; 30 Comprehensive Cancer Center Munich, Munich, Germany; 31 Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Germany; 32 Department of Radiology, University Hospital, LMU Munich, Germany; 33 Department of Hematology/Oncology & Comprehensive Cancer Center Munich, LMU Klinikum Munich, Germany; 34 Polytechnique Montréal, Mila, QC, Canada; 35 Department of Diagnostic Radiology, Sheba Medical Center, Tel Aviv university, Israel; 36 Department of Surgical Data Science, Institut de Recherche contre les Cancers de l’Appareil Digestif (IRCAD), France; 37 Rafael Advanced Defense System, Israel; 38 Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel; 39 Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China; 40 Department of Electrical and Electronic Engineering, The University of Hong Kong, China; 41 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; 42 Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Sweden; 43 Barcelona Supercomputing Center, Barcelona, Spain; 44 Eidgenössische Technische Hochschule Zurich (ETHZ), Zurich, Switzerland; 45 Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Catalonia, Spain; 46 Universitat Politecnica de Catalunya, Catalonia, Spain; 47 University of Tuebingen, Germany; 48 Mannheim Institute for Intelligent Systems in Medicine, department of Medicine Mannheim, Heidelberg University, Germany; 49 Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; 50 Central Institute for Computer Engineering (ZITI), Heidelberg University, Germany; 51 Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany; 52 Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, USA; 53 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, South Korea; 54 Department of Mathematics, Nanjing University of Science and Technology, China; 55 Department of Mathematics, Nanjing University, China; 56 School of Information and Communication Engineering, University of Electronic Science and Technology of China, China; 57 Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany; 58 Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong, China; 59 Beckman Institute, University of Illinois at Urbana-Champaign, USA; 60 Siemens Healthineers, USA; 61 School of Computer Science, the University of Sydney, Australia; 62 Fraunhofer MEVIS, Bremen, Germany; 63 Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany; 64 Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands; 65 Medical Image Computing Group, FB3, University of Bremen, Germany; 66 Departments of Mathematics, Biomedical Engineering, University of California, Irvine, USA; 67 Center for Complex Biological Systems, University of California, Irvine, USA; 68 Chao Family Comprehensive Cancer Center, University of California, Irvine, USA; 69 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; 70 Helmholtz Imaging, Germany; 71 NVIDIA, Santa Clara, CA, USA; 72 Department of Computer Science, University of Copenhagen, Denmark; 73 Kakao Brain, Republic of Korea; 74 Institute of Imaging & Computer Vision, RWTH Aachen University, Germany; 75 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China; 76 Department of Radiation Oncology and Radiotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany; 77 Department of computer science, UNC Chapel Hill, USA; 78 TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, GermanyPostprint (published version

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications