Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary care facilities in rural areas and during crises. The recently emerging field of Neural Cellular Automata (NCA) has shown that locally interacting one-cell models can achieve competitive results in tasks such as image generation or segmentations in low-resolution inputs. However, they are constrained by high VRAM requirements and the difficulty of reaching convergence for high-resolution images. To counteract these limitations we propose Med-NCA, an end-to-end NCA training pipeline for high-resolution image segmentation. Our method follows a two-step process. Global knowledge is first communicated between cells across the downscaled image. Following that, patch-based segmentation is performed. Our proposed Med-NCA outperforms the classic UNet by 2% and 3% Dice for hippocampus and prostate segmentation, respectively, while also being 500 times smaller. We also show that Med-NCA is by design invariant with respect to image scale, shape and translation, experiencing only slight performance degradation even with strong shifts; and is robust against MRI acquisition artefacts. Med-NCA enables high-resolution medical image segmentation even on a Raspberry Pi B+, arguably the smallest device able to run PyTorch and that can be powered by a standard power bank.
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（人工智能与健康研究所，慕尼黑德国环境健康研究中心）；TUM School of Computation, Information and Technology, Technical University Munich（技术大学慕尼黑计算、信息与技术学院）；Institute of Chemical Epigenetics, Faculty of Chemistry and Pharmacy, Ludwig Maximilian University（化学表观遗传学研究所，路德维希-马克西米利安大学）；Computer Aided Medical Procedures, Technical University of Munich（医学辅助程序研究所，技术大学慕尼黑）
Attention Pooling Enhances NCA-based Classification of Microscopy Images
Institute of AI for Health, Computational Health Center, Helmholtz Munich, Munich, Germany（人工智能与健康研究所，计算健康中心，海德堡慕尼黑研究中心，慕尼黑，德国）；Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（计算机辅助医疗程序，慕尼黑技术大学，慕尼黑，德国）；Institute of Pathology, Technical University Munich, Munich, Germany（病理研究所，慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）
Temporal Neural Cellular Automata: Application to modeling of contrast enhancement in breast MRI
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany School of Computation, Information；Technology, Technical University of Munich, Germany Departament de Matematiques i Informatica, Universitat de Barcelona, Spain Institució Catalana de Recerca i Estudis Avan c ats (ICREA), Barcelona, Spain Institute for Diagnostic；Interventional Radiology, School of Medicine \& Health, Klinkum Rechts der Isar, Technical University of Munich, Germany German Cancer Consortium (DKTK), Partner Site Munich, Germany School of Biomedical Engineering \& Imaging Sciences, King's College London, UK
Measuring Prediction Uncertainty in Neural Cellular Automata
Computational Health Center, Helmholtz Munich, Neuherberg, Germany（赫尔姆霍茨慕尼黑计算健康中心）；Helmholtz AI, Helmholtz Munich, Neuherberg, Germany（赫尔姆霍茨慕尼黑人工智能研究所）；Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（慕尼黑技术大学计算机辅助医疗程序研究所）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department of Medicine III, Ludwig-Maximilian-University Hospital, Munich, Germany（慕尼黑路德维希-马克西米利安大学医院第三医学部）；Department of Physics, University of Munich, Munich, Germany（慕尼黑大学物理系）；German Cancer Consortium (DKTK), partner site Munich, Germany（德国癌症研究中心（DKTK）慕尼黑分部）
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（慕尼黑亥姆霍兹中心人工智能健康研究所（德国环境健康研究中心））；Faculty of Mathematics, Technical University Munich（慕尼黑工业大学数学学院）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Computer Aided Medical Procedures, Johns Hopkins University（约翰霍普金斯大学计算机辅助医疗程序研究所）；Helmholtz AI, Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹人工智能中心（慕尼黑亥姆霍兹中心，德国环境健康研究中心））
Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge detection of binary images based on two dimensional twenty five neighborhood cellular automata. The method considers only linear rules of CA for extraction of edges under null boundary condition. The performance of this approach is compared with some existing edge detection techniques. This comparison s…
Efficient Ultrasound Image Segmentation with Token-Conditioned Neural Cellular Automata
School of Medicine, University College Dublin（都柏林大学医学院）；Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究培训中心）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）；Department of Computer Science, Maynooth University（梅努斯大学计算机科学系）；Departament de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机科学系）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究机构）
Department of Computer Science, University of Copenhagen（计算机科学系，哥本哈根大学）；Department of Oncology, Rigshospitalet（肿瘤学系，哥本哈根大学医院）；Department of Radiology and Nuclear Medicine, Erasmus MC – University Medical Center Rotterdam（放射学与核医学系，埃因霍温麦斯特大学医学中心 Rotterdam）
University of Marburg（马尔堡大学）；TU Graz（格拉茨技术大学）；BioTechMed（生物技术医学）
Due to the aging population, spinal diseases get more and more common nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States. Thus the numbers of surgical spinal procedures are also increasing with the aging population and precise diagnosis plays a vital role in reducing complication and recurrence of symptoms. Spinal imaging of vertebral column is a tedious process subjected to interpretation errors. In this contribution, we aim to reduce time and error for vertebral interpretation by applying and studying the GrowCut-algorithm…
