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A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer

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arXiv 2024年

作者： Machado, Inês P. Reithmeir, Anna Kogl, Fryderyk Rundo, Leonardo Funingana, Gabriel Reinius, Marika Mungmeeprued, Gift Gao, Zeyu McCague, Cathal Kerfoot, Eric Woitek, Ramona Sala, Evis Ou, Yangming Brenton, James Schnabel, Julia Crispin, Mireia Department of Oncology University of Cambridge United Kingdom Cancer Research UK Cambridge Institute University of Cambridge United Kingdom Early Cancer Institute University of Cambridge United Kingdom School of Computation Information & Technology Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany Department of Information and Electrical Engineering University of Salerno Italy School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Research Center for Medical Image Analysis and AI Danube University Austria Department of Radiologic Sciences Università Cattolica del Sacro Cuore Italy Department of Radiology Boston Children’s Hospital Harvard Medical School United States

High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, typically manifesting at an advanced metastatic stage. A major challenge in treating advanced HGSOC is effectively monitoring localised change in tumour burden across multiple sites during neoadjuvant chemotherapy (NACT) and predicting long-term pathological response and overall patient survival. In this work, we propose a self-supervised deformable image registration algorithm that utilises a general-purpose image encoder for image feature extraction to co-register contrast-enhanced computerised tomography scan images acquired before and after neoadjuvant chemotherapy. This approach addresses challenges posed by highly complex tumour deformations and longitudinal lesion matching during treatment. Localised tumour changes are calculated using the Jacobian determinant maps of the registration deformation at multiple disease sites and their macroscopic areas, including hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), and intermediate density (i.e., soft tissue) portions. A series of experiments is conducted to understand the role of a general-purpose image encoder and its application in quantifying change in tumour burden during neoadjuvant chemotherapy in HGSOC. This work is the first to demonstrate the feasibility of a self-supervised image registration approach in quantifying NACT-induced localised tumour changes across the whole disease burden of patients with complex multisite HGSOC, which could be used as a potential marker for ovarian cancer patient’s long-term pathological response and survival. © 2024, CC BY.

关键词： Tumors

Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts

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arXiv 2024年

作者： Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Bernard and Irene Schwartz Center for Biomedical Imaging Dept. of Radiology NYU School of Medicine NY United States Dept. of Radiology NYU School of Medicine NY United States CUBRIC School of Engineering Cardiff University Cardiff United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Computation Information and Technology Technical University of Munich Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Neurobiology Research Unit Copenhagen University Hospital Copenhagen Denmark

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics’ robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. © 2024, CC BY.

关键词： Image quality

Causal machine learning for predicting treatment outcomes

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arXiv 2024年

作者： Feuerriegel, Stefan Frauen, Dennis Melnychuk, Valentyn Schweisthal, Jonas Hess, Konstantin Curth, Alicia Bauer, Stefan Kilbertus, Niki Kohane, Isaac S. van der Schaar, Mihaela LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany Department of Applied Mathematics & Theoretical Physics University of Cambridge Cambridge United Kingdom School of Computation Information and Technology TU Munich Munich Germany Helmholtz Munich Munich Germany Department of Biomedical Informatics Harvard Medical School Boston United States Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom The Alan Turing Institute London United Kingdom

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes. Here, we present how methods from causal ML can be used to understand the effectiveness of treatments, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that allows for estimating individualized treatment effects, as well as personalized predictions of potential patient outcomes under different treatments. This offers granular insights into when treatments are effective, so that decision-making in patient care can be personalized to individual patient profiles. We further discuss how causal ML can be used in combination with both clinical trial data as well as real-world data such as clinical registries and electronic health records. We finally provide recommendations for the reliable use of causal ML in medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

Advancing Neonatal Care: A Deep learning Approach for Non-Contact Heart Rate Monitoring

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Advancing Neonatal Care: A Deep Learning Approach for Non-Co...

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Alex Grafton Alejandra Castelblanco Joana M. Warnecke Lynn Thomson Benjamin Schubert Anne Hilgendorff Julia A. Schnabel Joan Lasenby Kathryn Beardsall Engineering Department Signal Processing and Communications Laboratory Cambridge University UK Computational Health Center Munich Germany School of Computation Information and Technology Technical University of Munich Germany Department of Paediatrics University of Cambridge UK Institute of Lung Health and Immunity Helmholtz Center Munich. Comprehensive Pneumology Center Munich Member of the German Center of Lung Research (DZL) Munich Germany Dr. von Hauner Children's Hospital Hospital of the Ludwig Maximilian Universität München Munich Germany School of Biomedical Engineering and Imaging Sciences King's College London UK Institute of Machine Learning in Biomedical Imaging Munich Germany

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Heart rate is an important indicator of newborn health status. Conventional wired heart rate monitoring is affected by motion, can limit parental bonding and is prone to damage the fragile newborn skin. Video-based heart rate monitoring in adults has shown potential for the assessment of cardiac functions in optimal acquisition conditions. However, automated methods adapted for neonates, trained with limited sample sizes and capable of handling occlusions and variable illumination, still need to be explored. This work proposes a new deep-learning pipeline for video-based neonatal heart rate measurement, integrating color and infrared signals from mul-tiple neonatal body regions. We train deep learning models for pose estimation and heart rate detection in a pilot cohort of five neonates recorded in the clinic for up to 60 minutes. Our methods show generalization in a leave-one-out cross-validation scheme; the newborn pose estimation model presents high performance (average precision=0.85±0.07), while the heart rate detection model achieves a mean absolute error of 3.83±1.22 beats per minute compared to the electrocardiogram heart rate. Automated video-based heart rate measurement could provide a non-contact, low-cost complement to current HR monitoring technologies in the clinic or outpatient settings.

关键词： Deep learning Pediatrics Pose estimation Pipelines Lighting Heart rate detection Photoplethysmography Skin Heart rate measurement Monitoring

Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks

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arXiv 2024年

作者： Osuala, Richard Joshi, Smriti Tsirikoglou, Apostolia Garrucho, Lidia Pinaya, Walter H.L. Lang, Daniel M. Schnabel, Julia A. Diaz, Oliver Lekadir, Karim Departament de Matemàtiques i Informàtica Universitat de Barcelona Spain Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden King’s College London London United Kingdom Computer Vision Center Universitat Autònoma de Barcelona Bellaterra Spain Passeig Lluís Companys 23 Barcelona Spain

Purpose: Deep generative models and synthetic data generation have become essential for advancing computer-assisted diagnosis and treatment. We explore one such emerging and particularly promising application of deep generative models, namely, the generation of virtual contrast enhancement. This allows to predict and simulate contrast enhancement in breast magnetic resonance imaging (MRI) without physical contrast agent injection, thereby unlocking lesion localization and categorization even in patient populations where the lengthy, costly and invasive process of physical contrast agent injection is contraindicated. Approach: We define a framework for desirable properties of synthetic data, which leads us to propose the Scaled Aggregate Measure (SAMe) consisting of a balanced set of scaled complementary metrics for generative model training and convergence evaluation. We further adopt a conditional generative adversarial network to translate from non-contrast-enhanced T1-weighted fat-saturated breast MRI slices to their dynamic contrast-enhanced (DCE) counterparts, thus learning to detect, localize, and adequately highlight breast cancer lesions. Next, we extend our model approach to jointly generate multiple DCE-MRI timepoints, enabling the simulation of contrast enhancement across temporal DCE-MRI acquisitions. Additionally, 3D U-Net tumor segmentation models are implemented and trained on combinations of synthetic and real DCE-MRI data to investigate the effect of data augmentation with synthetic DCE-MRI volumes. Results: Conducting four main sets of experiments, (i) the variation across single metrics demonstrated the value of SAMe, and (ii) the quality and potential of virtual contrast injection for tumor detection and localization were shown. Segmentation models (iii) augmented with synthetic DCE-MRI data were more robust in the presence of domain shifts between pre-contrast and DCE-MRI domains. The joint synthesis approach of multi-sequence DCE-MRI (iv) resul

关键词： Generative adversarial networks

LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

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arXiv 2024年

作者： Dorent, Reuben Khajavi, Roya Idris, Tagwa Ziegler, Erik Somarouthu, Bhanusupriya Jacene, Heather LaCasce, Ann Deissler, Jonathan Ehrhardt, Jan Engelson, Sofija Fischer, Stefan M. Gu, Yun Handels, Heinz Kasai, Satoshi Kondo, Satoshi Maier-Hein, Klaus Schnabel, Julia A. Wang, Guotai Wang, Litingyu Wald, Tassilo Yang, Guang-Zhong Zhang, Hanxiao Zhang, Minghui Pieper, Steve Harris, Gordon Kikinis, Ron Kapur, Tina Brigham and Women’s Hospital Harvard Medical School BostonMA United States Massachusetts General Hospital Harvard Medical School BostonMA United States Yunu Inc. CaryNC United States Isomics Inc CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States Technical University Munich Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Medical Informatics University of Lübeck Lübeck Germany German Research Center for Artificial Intelligence Lübeck Germany Niigata University of Health and Welfare Niigata Japan Muroran Institute of Technology Hokkaido Japan Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Electronic Science and Technology of China Chengdu China Heidelberg Germany University of Heidelberg Heidelberg Germany Shanghai AI laboratory Shanghai China

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCai 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of 61.0%. On the other hand, top-ranked teams, with a median Dice score exceeding 70%, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance. . . © 2024, CC BY.

关键词： Image segmentation

Confidence intervals uncovered: Are we ready for real-world medical imaging ai?

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arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the ai transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCai segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCai challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCai 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics

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arXiv 2024年

作者： Banerjee, Oishi Saenz, Agustina Wu, Kay Clements, Warren Zia, Adil Buensalido, Dominic Kavnoudias, Helen Abi-Ghanem, Alain S. El Ghawi, Nour Luna, Cibele Castillo, Patricia Al-Surimi, Khaled Daghistani, Rayyan A. Chen, Yuh-Min Chao, Heng-Sheng Heiliger, Lars Kim, Moon Haubold, Johannes Jonske, Frederic Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Radiology Alfred Health MelbourneVIC Australia Department of Diagnostic Radiology American University of Beirut Beirut Lebanon Department of Radiology University of Miami Miller School of Medicine MiamiFL United States University of Miami Jackson Memorial Hospital MiamiFL United States Department of Healthcare Management University of Doha for Science and Technology Doha Qatar Department of Medical Imaging King Abdulaziz Medical City Riyadh Saudi Arabia Department of Chest Medicine Taipei Veterans General Hospital Taipei Taiwan Institute for AI in Medicine University Hospital Essen North Rhine-Westphalia Essen Germany Department of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen North Rhine-Westphalia Essen Germany Department of Medical Machine Learning Institute of AI in Medicine University Medicine Essen North Rhine-Westphalia Essen Germany MAIDA Initiative Partners

Given the rapidly expanding capabilities of generative ai models for radiology, there is a need for robust metrics that can accurately measure the quality of ai-generated radiology reports across diverse hospitals. We develop ReXamine-Global, a LLM-powered, multi-site framework that tests metrics across different writing styles and patient populations, exposing gaps in their generalization. First, our method tests whether a metric is undesirably sensitive to reporting style, providing different scores depending on whether ai-generated reports are stylistically similar to ground-truth reports or not. Second, our method measures whether a metric reliably agrees with experts, or whether metric and expert scores of ai-generated report quality diverge for some sites. Using 240 reports from 6 hospitals around the world, we apply ReXamine-Global to 7 established report evaluation metrics and uncover serious gaps in their generalizability. Developers can apply ReXamine-Global when designing new report evaluation metrics, ensuring their robustness across sites. Additionally, our analysis of existing metrics can guide users of those metrics towards evaluation procedures that work reliably at their sites of interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics

(Predictable) Performance Bias in Unsupervised Anomaly Detection

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arXiv 2023年

作者： Meissen, Felix Breuer, Svenja Knolle, Moritz Buyx, Alena Müller, Ruth Kaissis, Georgios Wiestler, Benedikt Rückert, Daniel Chair for AI in Healthcare and Medicine Klinikum rechts der Isar der Technischen Universität München Einsteinstr. 25 Munich81675 Germany Department of Science Technology and Society School of Social Sciences and Technology Technical University of Munich Arcisstr. 21 Munich80333 Germany Department of Economics and Policy School of Management Technical University of Munich Arcisstraße 21 Munich80333 Germany Walther-von-Dyck-Str. 10 Garching85748 Germany Institute for History and Ethics of Medicine School of Medicine Technical University of Munich Prinzregentenstraße 68 Munich81675 Germany Institute for Machine Learning in Biomedical Imaging Helmholtz Munich Ingolstädter Landstraße 1 Neuherberg85764 Germany Department of Diagnostic and Interventional Neuroradiology Klinikum rechts der Isar Ismaninger Str. 22 Munich81675 Germany Department of Computing Imperial College London LondonSW7 2AZ United Kingdom TranslaTUM Center for Translational Cancer Research Technical University of Munich Ismaninger Str. 22 Munich81675 Germany

Background With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored. Methods In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced a novel subgroup-AUROC (sAUROC) metric, which aids in quantifying fairness in machine learning. Findings Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups. Interpretation Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical "fairness laws" discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition. Funding European Research Council Deep4MI © 2023, CC BY-NC-SA.

关键词： Anomaly detection