Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes
University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Donostia International Physics Center (DIPC)（多诺斯蒂亚国际物理中心（DIPC））；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁健康研究所）；Ikerbasque, Basque Foundation for Science（巴斯克科学基金会（Ikerbasque））
Daniel Franco-Barranco, Arrate Muñoz-Barrutia, Ignacio Arganda-Carreras
Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications do not make neither the code nor the full training details public to support the results obtained, leading to reproducibility issues and dubious model comparisons. For that reason, and following a recent code of best practices for reporting experimental results, we present an extensive study of the state-of-the-art deep learning architectures for the segmentation of mitochondria on EM volumes, and evaluate the impact in performance of different variations of 2D and 3D U-Net-like models for this task. To better understand the contribution of each component, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters values for all architectures have been performed and each configuration has been run multiple times to report the mean and standard deviation values of the evaluation metrics. Using this methodology, we found very stable architectures and hyperparameter configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset. Furthermore, we have benchmarked our proposed models on two other available datasets, Lucchi++ and Kasthuri++, where they outperform all previous works. The code derived from this research and its documentation are publicly available.
Deep learning based domain adaptation for mitochondria segmentation on EM volumes
University of the Basque Country (UPV/EHU)（巴斯克大学）；Donostia International Physics Center (DIPC)（多诺斯蒂亚国际物理中心）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁卫生研究所）；Ikerbasque, Basque Foundation for Science（巴斯克科学基金会）
AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware
Department of Laboratory Medicine & Pathology, University of Washington（华盛顿大学检验医学与病理学系）；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Electrical Engineering & Computer Science, Oregon State University（俄勒冈州立大学电气工程与计算机科学系）
Self-supervised Pretraining of Cell Segmentation Models
School of Computer Science, Georgia Institute of Technology（佐治亚理工学院计算机科学学院）；School of Materials Science and Engineering, Georgia Institute of Technology（佐治亚理工学院材料科学与工程学院）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程学院）
Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets
TU Dresden（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus（卡尔·古斯塔夫·卡鲁斯大学医院及医学院）；Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich（慕尼黑亥姆霍兹中心德累斯顿保罗·朗格汉斯研究所（PLID））；German Center for Diabetes Research (DZD e.V.)（德国糖尿病研究中心（DZD e.V.））；Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association（亥姆霍兹联合会马克斯·德尔布吕克分子医学中心（MDC））；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Going Beyond U-Net: Assessing Vision Transformers for Semantic Segmentation in Microscopy Image Analysis
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）；Center for Artificial Intelligence, Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学人工智能中心）；STACC
Embedding-based Instance Segmentation in Microscopy
Max Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）；IT4Innovations, VŠB - Technical University of Ostrava（IT4Innovations，俄斯特拉发技术大学）；Fondatione Human Technopole（人类技术基金会）
Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line
University of South Bohemia in České Budějovice（捷克布杰约维采南波希米亚大学）；Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences（亥姆霍兹波茨坦中心德国地球科学研究中心）
Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation
PSL University（巴黎文理研究大学）；Mines Paris（巴黎矿业学院）；Centre for Computational Biology（计算生物学中心）；Institut Curie（居里研究所）；INSERM（法国国家健康与医学研究院）；University of Montpellier（蒙彼利埃大学）；IGH（人类遗传学研究所）；CNRS（法国国家科学研究中心）
Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence（佛罗伦萨大学欧洲非线性光谱实验室）；Bioretics SrL（Bioretics SrL公司）；National Institute of Optics, National Research Council (INO-CNR)（国家研究委员会国家光学研究所）；Department of Physics（物理系）
Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy
The Fifth Affiliated Hospital of Wenzhou Medical University（温州医科大学附属第五医院）；Tsinghua University（清华大学）；Hangzhou Dianzi University（杭州电子科技大学）；Shandong University（山东大学）
Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
Wayne State University（韦恩州立大学）；NanoBioScience Institute（纳米生物科学研究所）；College of Engineering（工学院）；School of Medicine（医学院）；Center for Molecular Medicine & Genetics（分子医学与遗传学中心）；University of Vermont College of Medicine（佛蒙特大学医学院）；Karolinska Institute（卡罗林斯卡学院）
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Sabanci University（萨班哲大学）；ARM Ltd.（ARM有限公司）；University of Zurich（苏黎世大学）；University of Utah（犹他大学）；Izmir University of Economics（伊兹密尔经济大学）；University of Rochester（罗切斯特大学）
DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae
Delft University of Technology（代尔夫特理工大学）；Rotterdam Eye Hospital（鹿特丹眼科医院）；Amphia Hospital（安菲娅医院）；Erasmus Medical Center Rotterdam（鹿特丹伊拉斯谟医学中心）；Novo Research Consultancy（诺沃研究咨询公司）
gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy
Department of Information Technology, Uppsala University（信息科技系，乌普萨拉大学）；Biomedical Imaging Unit, A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland（生物医学成像单元，A.I.维塔纳分子科学研究所，东芬兰大学）；Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki（电子显微镜单元，生物技术研究所，赫尔辛基大学）
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation
Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, German Research Center for Environmental Health（亥姆霍兹慕尼黑中心环境健康研究所组织工程与再生医学研究所）；TUM School of Computation, Information and Technology (CIT), Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University of Zurich（苏黎世大学）；Shanghai University of Finance and Economics（上海财经大学）；BGI Research（华大基因研究院）；Zhejiang University（浙江大学）；Sunnybrook Research Institute, University of Toronto（多伦多大学桑尼布鲁克研究所）
