Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Generative Adversarial Networks based Skin Lesion Segmentation

Skin cancer is a serious condition that requires accurate identification and treatment. One way to assist clinicians in this task is by using computer-aided diagnosis (CAD) tools that can automatically segment skin lesions from dermoscopic images. To this end, a new adversarial learning-based framework called EGAN has been developed. This framework uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path and an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. Additionally, a morphology-based smoothing loss is implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018 and outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. This represents a 2% increase in Dice Coefficient, 1% increase in Jaccard Index, and 1% increase in Accuracy

QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation -- analysis of ranking metrics and benchmarking results

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This metric (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QUBraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTSResearch reported in this publication was partly supported by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), under award numbers NIH/NCI/ITCR:U01CA242871 and NIH/NCI/ITCR:U24CA189523. It was also partly supported by the National Institute of Neurological Disorders and Stroke (NINDS) of the NIH, under award number NIH/NINDS:R01NS042645.Document signat per 92 autors/autores: Raghav Mehta1 , Angelos Filos2 , Ujjwal Baid3,4,5 , Chiharu Sako3,4 , Richard McKinley6 , Michael Rebsamen6 , Katrin D¨atwyler6,53, Raphael Meier54, Piotr Radojewski6 , Gowtham Krishnan Murugesan7 , Sahil Nalawade7 , Chandan Ganesh7 , Ben Wagner7 , Fang F. Yu7 , Baowei Fei8 , Ananth J. Madhuranthakam7,9 , Joseph A. Maldjian7,9 , Laura Daza10, Catalina Gómez10, Pablo Arbeláez10, Chengliang Dai11, Shuo Wang11, Hadrien Raynaud11, Yuanhan Mo11, Elsa Angelini12, Yike Guo11, Wenjia Bai11,13, Subhashis Banerjee14,15,16, Linmin Pei17, Murat AK17, Sarahi Rosas-González18, Illyess Zemmoura18,52, Clovis Tauber18 , Minh H. Vu19, Tufve Nyholm19, Tommy L¨ofstedt20, Laura Mora Ballestar21, Veronica Vilaplana21, Hugh McHugh22,23, Gonzalo Maso Talou24, Alan Wang22,24, Jay Patel25,26, Ken Chang25,26, Katharina Hoebel25,26, Mishka Gidwani25, Nishanth Arun25, Sharut Gupta25 , Mehak Aggarwal25, Praveer Singh25, Elizabeth R. Gerstner25, Jayashree Kalpathy-Cramer25 , Nicolas Boutry27, Alexis Huard27, Lasitha Vidyaratne28, Md Monibor Rahman28, Khan M. Iftekharuddin28, Joseph Chazalon29, Elodie Puybareau29, Guillaume Tochon29, Jun Ma30 , Mariano Cabezas31, Xavier Llado31, Arnau Oliver31, Liliana Valencia31, Sergi Valverde31 , Mehdi Amian32, Mohammadreza Soltaninejad33, Andriy Myronenko34, Ali Hatamizadeh34 , Xue Feng35, Quan Dou35, Nicholas Tustison36, Craig Meyer35,36, Nisarg A. Shah37, Sanjay Talbar38, Marc-Andr Weber39, Abhishek Mahajan48, Andras Jakab47, Roland Wiest6,46 Hassan M. Fathallah-Shaykh45, Arash Nazeri40, Mikhail Milchenko140,44, Daniel Marcus40,44 , Aikaterini Kotrotsou43, Rivka Colen43, John Freymann41,42, Justin Kirby41,42, Christos Davatzikos3,4 , Bjoern Menze49,50, Spyridon Bakas∗3,4,5 , Yarin Gal∗2 , Tal Arbel∗1,51 // 1Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada, 2Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England, 3Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA, 4Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA, 5Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 6Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland, 7Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA, 8Department of Bioengineering, University of Texas at Dallas, Texas, USA, 9Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, 10Universidad de los Andes, Bogotá, Colombia, 11Data Science Institute, Imperial College London, London, UK, 12NIHR Imperial BRC, ITMAT Data Science Group, Imperial College London, London, UK, 13Department of Brain Sciences, Imperial College London, London, UK, 14Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India, 15Department of CSE, University of Calcutta, Kolkata, India, 16 Division of Visual Information and Interaction (Vi2), Department of Information Technology, Uppsala University, Uppsala, Sweden, 17Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA, 18UMR U1253 iBrain, Université de Tours, Inserm, Tours, France, 19Department of Radiation Sciences, Ume˚a University, Ume˚a, Sweden, 20Department of Computing Science, Ume˚a University, Ume˚a, Sweden, 21Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain, 22Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand, 23Radiology Department, Auckland City Hospital, Auckland, New Zealand, 24Auckland Bioengineering Institute, University of Auckland, New Zealand, 25Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA, 26Massachusetts Institute of Technology, Cambridge, MA, USA, 27EPITA Research and Development Laboratory (LRDE), France, 28Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA, 29EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicˆetre, France, 30School of Science, Nanjing University of Science and Technology, 31Research Institute of Computer Vision and Robotics, University of Girona, Spain, 32Department of Electrical and Computer Engineering, University of Tehran, Iran, 33School of Computer Science, University of Nottingham, UK, 34NVIDIA, Santa Clara, CA, US, 35Biomedical Engineering, University of Virginia, Charlottesville, USA, 36Radiology and Medical Imaging, University of Virginia, Charlottesville, USA, 37Department of Electrical Engineering, Indian Institute of Technology - Jodhpur, Jodhpur, India, 38SGGS ©2021 Mehta et al.. License: CC-BY 4.0. arXiv:2112.10074v1 [eess.IV] 19 Dec 2021 Mehta et al. Institute of Engineering and Technology, Nanded, India, 39Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center, 40Department of Radiology, Washington University, St. Louis, MO, USA, 41Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA, 42Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, 43Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 44Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA, 45Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, USA, 46Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland, 47Center for MR-Research, University Children’s Hospital Zurich, Zurich, Switzerland, 48Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India, 49Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, 50Department of Informatics, Technical University of Munich, Munich, Germany, 51MILA - Quebec Artificial Intelligence Institute, Montreal, QC, Canada, 52Neurosurgery department, CHRU de Tours, Tours, France, 53 Human Performance Lab, Schulthess Clinic, Zurich, Switzerland, 54 armasuisse S+T, Thun, Switzerland.Preprin

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy