We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.
We apply an ensemble of modified TransBTS, nnU-Net, and a combination of both for the segmentation task of the BraTS 2021 challenge. In fact, we change the original architecture of the TransBTS model by adding Squeeze-and-Excitation blocks, an increasing number of CNN layers, replacing positional encoding in Transformer block with a learnable Multilayer Perceptron (MLP) embeddings, which makes Transformer adjustable to any input size during inference. With these modifications, we are able to largely improve TransBTS performance. Inspired by a nnU-Net framework we decided to combine it with our…
Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院，数学科学学院，MOE-LSC，上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科，华山医院，复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科，华山医院，复旦大学）
Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI
Multimedia University（多媒体大学）；University of British Columbia（不列颠哥伦比亚大学）；McGill University（麦吉尔大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Federal University of Technology Minna（明纳联邦理工大学）；Medical Artificial Intelligence Laboratory（医学人工智能实验室）；Friedrich-Alexander-Universität（弗里德里希-亚历山大大学）
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Sharif University of Technology（谢里夫理工大学）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心人工智能医学研究所）；Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心/LASI）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR)（埃森大学医学中心（公法团体）科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；TU Dortmund University（多特蒙德工业大学）
Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院、数学科学学院、MOE-LSC、上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科、华山医院、复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科、华山医院、复旦大学）
Southern Medical University（南方医科大学）；LaTIM（拉蒂姆实验室）；INSERM（法国国家健康与医学研究院）；Univ Brest（布雷斯特大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）
Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes plays a crucial role in the optimization treatment strategy and prognosis analysis. This study aims to employ nnU-Net for automatic segmentation and radiomics for recurrence-free survival (RFS) prediction using pretreatment PET/CT images in multi-center HNC cohort. A multi-center HNC dataset with 883 patients (524 patients for training, 359 for testing) was provided in HECKTOR 2022. A bounding box of the extended oropharyngeal region was retrieved for each patient with fixed size of 224 x 224 x 224 $mm^{3}$. Then 3D nn…
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Institute of Engineering, Thapathali Campus, Nepal（尼泊尔工程学院，塔帕塔利校区）；Gandaki College of Engineering and Science, Pokhara University, Pokhara, Nepal（加德基工程与科学学院，博克拉大学，博克拉，尼泊尔）；Nepal Engineering College, Changunarayan-4, Bhaktapur, Nepal（尼泊尔工程学院，恰纳拉亚纳-4，巴赫塔普尔，尼泊尔）；Madan Bhandari University of Science and Technology, Chitlang, Nepal（马丹·班达里科学与技术大学，奇塔朗，尼泊尔）；Montreal Neurological Institute, McGill University, Montreal, QC, Canada（蒙特利尔神经研究所，麦吉尔大学，蒙特利尔，魁北克，加拿大）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）；Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria（医学人工智能实验室（MAI实验室），拉各斯，尼日利亚）；Department of Physics, Federal University of Technology, Minna, Nigeria（物理系，联邦技术大学，米纳，尼日利亚）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Department of Radiology and Radiological Science, Johns Hopkins School of Medicine（放射学与放射科学系，约翰霍普金斯医学院）；Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Nepal（尼泊尔应用数学与信息学研究所（NAAMII），尼泊尔）；Institute of Engineering, Purbanchal Campus, Nepal（尼泊尔工程学院，普尔班恰尔校区）
College of Medicine and Biological Information Engineering, Northeastern University（东北大学生物医学与信息工程学院）；Department of Electrical and Computer Engineering, Steven Institute of Technology（史蒂文斯理工学院电气与计算机工程系）；Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education（医学影像智能计算教育部重点实验室）
Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall se…
Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and…
Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data plays an important role in assessing tumor response to therapy and personalized treatment stratification.Manual segmentation is tedious and subjective.Deep-learning-based algorithms for brain tumor segmentation have the potential to provide objective and fast tumor segmentation.However, the training of such algorithms requires large datasets which are not always available. Data augmentation techniques may reduce the need for large datasets.However current approaches are mostly parametric and may result in suboptimal …
School of Biomedical Engineering（生物医学工程学院）；Universidad Autónoma de Ciudad Juárez（华雷斯自治大学）
The brain is a complex organ controlling cognitive process and physical functions. Tumors in the brain are accelerated cell growths affecting the normal function and processes in the brain. MRI scans provides detailed images of the body being one of the most common tests to diagnose brain tumors. The process of segmentation of brain tumors from magnetic resonance imaging can provide a valuable guide for diagnosis, treatment planning and prediction of results. Here we consider the problem brain tumor segmentation using a Deep learning architecture for use in tumor segmentation. Although the pro…
Shanghai United Imaging Intelligence Co. Ltd（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）
In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such …
Beihang University（北京航空航天大学）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
In the clinical diagnosis and treatment of brain tumors, manual image reading consumes a lot of energy and time. In recent years, the automatic tumor classification technology based on deep learning has entered people's field of vision. Brain tumors can be divided into primary and secondary intracranial tumors according to their source. However, to our best knowledge, most existing research on brain tumors are limited to primary intracranial tumor images and cannot classify the source of the tumor. In order to solve the task of tumor source type classification, we analyze the existing technolo…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the complexity and variety of tumors, shapes, and locations make their segmentation and classification complex. In this regard, numerous researchers have proposed brain tumor segmentation and classification methods. This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhan…
The brain tumor is the most aggressive kind of tumor and can cause low life expectancy if diagnosed at the later stages. Manual identification of brain tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment and thus reduce the chances of survival for the patient. Medical resonance imaging (MRI) is the conventional method used to diagnose brain tumors and their types. This paper attempts to eliminate the manual process from the diagnosis process and use machine learning instead. We proposed the use of pretrained convolutional neural networks (CNN) for the diagnosis and …
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge dataset, where we evaluated our proposal in the BraTS 2020 Validation and Tes…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. Therefore, accurate and timely diagnosis of brain tumors will lead to more effective treatments. Physicians classify brain tumors only with biopsy operation by brain surgery, and after diagnosing the type of tumor, a treatment plan is considered for the patient. Automatic systems based on machine learning algorithms can allow physicians to diagnose brain tumors with noninvasive…
Vingroup Big Data Institute (VinBigdata)（越南 VinBigdata 大数据研究院）；Hanoi University of Science and Technology (HUST)（河内科技大学）；University of Engineering and Technology (UET), VNU（越南国立大学工程技术大学）
Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS's validation set, it achieved an average Dice score of 78.43%, 89.99%, and 84.22% respectively for the enhancing tumo…
University of Electronic Science and Technology of China（电子科技大学）；New Jersey Institute of Technology（新泽西理工学院）
Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmenta…
Shenzhen University（深圳大学）；Huazhong University of Science and Technology（华中科技大学）
The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance…
University of Leicester（莱斯特大学）；University of Lancaster（兰卡斯特大学）；Queen Mary, University of London（伦敦大学玛丽女王学院）；Xidian University（西安电子科技大学）；Bielefeld University（比勒费尔德大学）
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to …
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment. In this paper, we propose a multi-step cascaded network which takes the hierarchical topology of the brain tumor substructures into consideration and segments the substructures from coarse to fine .During segmentation, the result of the former step is utilized as the prior information for the next step to guide the finer segmentation process. The whole network is trained in an end-to-end fashion. Besides, to alleviate the gradient vanishing issue and reduce o…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Data Science, Fudan University（复旦大学数据科学学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearance and ambiguous outlines of tumors. Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images. The MMTSN is composed of three sub-branches and a main branch. Specifically, the sub-branches are used to capture different tumor features from multi-modal images,…
Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R…
Huazhong University of Science and Technology（华中科技大学）；Shenzhen University（深圳大学）
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contextual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose…
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；Santa Luzia Hospital（圣卢西亚医院）
State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, for clinical practice, this last step is tedious and time-consuming and, therefore, not always feasible, resulting in skull-stripping faults that can negatively impact the tumor segmentation quality. Still, the extent of this impact has never been measured for any of the many diff…
Indian Institute of Information Technology Allahabad（印度阿拉哈巴德信息技术学院）
Brain tumor is the most common and deadliest disease that can be found in all age groups. Generally, MRI modality is adopted for identifying and diagnosing tumors by the radiologists. The correct identification of tumor regions and its type can aid to diagnose tumors with the followup treatment plans. However, for any radiologist analysing such scans is a complex and time-consuming task. Motivated by the deep learning based computer-aided-diagnosis systems, this paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MR…
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances ($95\%$…
Intracranial tumors are groups of cells that usually grow uncontrollably. One out of four cancer deaths is due to brain tumors. Early detection and evaluation of brain tumors is an essential preventive medical step that is performed by magnetic resonance imaging (MRI). Many segmentation techniques exist for this purpose. Low segmentation accuracy is the main drawback of existing methods. In this paper, we use a deep learning method to boost the accuracy of tumor segmentation in MR images. Cascade approach is used with multiple scales of images to induce both local and global views and help the…
Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process. Despite advances in medical image analyzing, accurate tumor segmentation in brain magnetic resonance (MR) images remains a challenge due to variations in tumor texture, position, and shape. In this paper, we propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. By adopting the out…
Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Especially, brain tumor diagnosis requires high accuracy, where minute errors in judgment may lead to disaster. For this reason, brain tumor segmentation is an important challenge for medical purposes. Currently several methods exist for tumor segmentation but they all lack high accuracy. Here we present a solution for brain tumor segmenting by using deep learning. In this work, we studied different angles of brain MR ima…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturb…
Chittagong University of Engineering and Technology（吉大港工程技术大学）
In recent advancement towards computer based diagnostics system, the classification of brain tumor images is a challenging task. This paper mainly focuses on elevating the classification accuracy of brain tumor images with transfer learning based deep neural network. The classification approach is started with the image augmentation operation including rotation, zoom, hori-zontal flip, width shift, height shift, and shear to increase the diversity in image datasets. Then the general features of the input brain tumor images are extracted based on a pre-trained transfer learning method comprised…
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects …
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Lenovo Research（联想研究院）
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment of brain tumor. However, previous methods mostly ignore the latent relationship among different modalities. In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation. Paralleled branches are designed to exploit different modality features and a series of layer connections are utilized to capture complex relationships and abundant information among modalities. We also us…
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
In our previous work, $i.e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved…
Korea Advanced Institute of Science and Technology (KAIST)（韩国高等科学技术研究院（KAIST））
Medical image analysis using deep neural networks has been actively studied. Deep neural networks are trained by learning data. For accurate training of deep neural networks, the learning data should be sufficient, of good quality, and should have a generalized property. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. Using healthy brain images, the …
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Baystate Medical Center（贝斯州医疗中心）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝斯州校区）
本文面向撒哈拉以南非洲成人患者，提出融合 UNet3D、ONet3D、SphereNet3D 及改进损失函数的十一模型集成脑肿瘤分割方法，在 BraTS AFRICA 数据上取得优于单模型的分割性能。
Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
The Netherlands Cancer Institute – Antoni van Leeuwenhoek（荷兰癌症研究所 – 安东尼·范·列文虎克）；National Center for Tumor Diseases (NCT), NCT/UCC Dresden（国家肿瘤疾病中心（NCT），NCT/UCC 德累斯顿）；DKFZ（德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学医学院及卡尔·古斯塔夫·卡鲁斯大学医院）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学触觉互联网与人机协同中心）；University of Twente（特文特大学）
