Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（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）；Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon（里昂高等师范学院里昂功能基因组学研究所）；Centre National de la Recherche Scientifique (CNRS)（法国国家科学研究中心）
Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, Gene Myers
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many automated methods for these tasks exist, they often struggle for images with low signal-to-noise ratios and/or dense packing of nuclei. It was recently shown for 2D microscopy images that these issues can be alleviated by training a neural network to directly predict a suitable shape representation (star-convex polygon) for cell nuclei. In this paper, we adopt and extend this approach to 3D volumes by using star-convex polyhedra to represent cell nuclei and similar shapes. To that end, we overcome the challenges of 1) finding parameter-efficient star-convex polyhedra representations that can faithfully describe cell nuclei shapes, 2) adapting to anisotropic voxel sizes often found in fluorescence microscopy datasets, and 3) efficiently computing intersections between pairs of star-convex polyhedra (required for non-maximum suppression). Although our approach is quite general, since star-convex polyhedra include common shapes like bounding boxes and spheres as special cases, our focus is on accurate detection and segmentation of cell nuclei. Finally, we demonstrate on two challenging datasets that our approach (StarDist-3D) leads to superior results when compared to classical and deep learning based methods.
Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland University（萨尔兰大学）
Segmentation of nanoscale electron microscopy (EM) images is crucial but still challenging in connectomics research. One reason for this is that none of the existing segmentation methods are error-free, so they require proofreading, which is typically implemented as an interactive, semi-automatic process via manual intervention. Herein, we propose a fully automatic proofreading method based on reinforcement learning that mimics the human decision process of detection, classification, and correction of segmentation errors. We systematically design the proposed system by combining multiple reinf…
Revisiting foundation models for cell instance segmentation
Georg-August-University Göttingen, Institute of Computer Science（哥廷根大学计算机科学学院）；CAIMed - Lower Saxony Center for AI & Causal Methods in Medicine, Göttingen（下萨克森人工智能与因果医学方法中心，哥廷根）；Cluster of Excellence Multiscale Bioimaging (MBExC), Georg-August-University Göttingen（多尺度生物成像（MBExC）集群，哥廷根大学）
Boundary-Aware Instance Segmentation in Microscopy Imaging
The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev（巴以大学电气与计算机工程学院）；Department of Systems Biology, Harvard Medical School（哈佛医学院系统生物学系）
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）
Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive pixel-wise annotation is a major limitation. To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift. In this study, we introduce an adversarial-prediction guided multi-task network to learn the adaptation of a well-trained model for use on a novel unlabeled target domain. Since no label is available on target domain, we learn an encoding representation…
Fraunhofer Institute for Computer Graphics Research (IGD)（弗劳恩霍夫计算机图形学研究所（IGD））；Ernst Moritz Arndt University（恩斯特·莫里茨·阿恩特大学）；Centre for Research on Ecology and Forestry Applications (CREAF)（生态与林业应用研究中心（CREAF））；University of Rostock（罗斯托克大学）
We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step.…
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；CSAIL, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region merging. Such functions are usually learned with supervised algorithms that demand considerable ground truth data, which are costly to collect. We propose a semi-supervised approach that reduces this demand. Based on a merge tree structure, we develop a differentiable unsupervised loss term that enforces consistent predictions from the learned function. We then propose a Bayesi…
EMBL Heidelberg（欧洲分子生物学实验室海德堡分部）；HCI University of Heidelberg（海德堡大学人机交互中心）；COS University of Heidelberg（海德堡大学计算机科学系）
The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural net…
Purdue University（普渡大学）；Indiana University-Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）；Indiana University（印第安纳大学）
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The prop…
De Montfort University（德蒙福特大学）；Instituto de Investigación Biomédica de Málaga – IBIMA（马拉加生物医学研究所）；Costa Rica Institute of Technology（哥斯达黎加理工学院）；University of Costa Rica（哥斯达黎加大学）；Rovira i Virgili University（罗维拉-威尔吉利大学）；University of Malaga（马拉加大学）
Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-know…
Universidad Peruana Cayetano Heredia（秘鲁卡耶塔诺·埃雷迪亚大学）；University of Padova（帕多瓦大学）
Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for autom…
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）
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility for segmenting microscopy data is currently little researched. Here we introduce EmbedSeg, an embedding-based instance segmentation method which outperforms existing state-of-the-art baselines on 2D as well as 3D microscopy datasets. Additionally, we show that EmbedSeg has a GPU memory footprint small enough to train even on la…
University of California Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background…
Sunnybrook Research Institute（桑尼布鲁克研究所）；Institute of Biomaterials and Biomedical Engineering, University of Toronto（多伦多大学生物材料与生物医学工程研究所）；University of Toronto（多伦多大学）
Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN fram…
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
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition process. We investigate the prediction of 3D cell instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly supervised method for volumetric instance segmentation of cells which only requires approximate cell centroids annotation. Created pseudo-labels are thereby refined with a novel refinement loss with Z-stack guidance. The evaluations show that our approach can generalize not only to BFM Z-Stack d…
TU Dresden（德累斯顿工业大学）；AICURA medical GmbH（AICURA医疗有限公司）；National Center of Tumor Diseases (NCT)（国家肿瘤疾病中心）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These labelled data sets are often difficult to acquire in the biomedical domain. In this work, we validate alternative ways to train CNNs with fewer labels for biomedical image segmentation using. We adapt two semi- and self-supervised image classification methods and analyse their performance for semantic segmentation of biomedical microscopy images.
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University Groningen（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；University Groningen（格罗宁根大学）；University Medical Center Groningen（格罗宁根大学医学中心）
Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；Harvard University（哈佛大学）
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural netw…
Max Delbrück Center for Molecular Medicine 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）
We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, na…
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
Harvard University（哈佛大学）；Technical University of Munich（慕尼黑工业大学）；MIT（麻省理工学院）；University of Cambridge（剑桥大学）；Donostia International Physics Center（多诺斯蒂亚国际物理中心）；University of the Basque Country（巴斯克大学）；Ikerbasque, Basque Foundation for Science（巴斯克科学基金会伊克巴斯克）；HHMI（霍华德·休斯医学研究所）；Boston College（波士顿学院）
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation jointly using a unified framework. Besides the CycleGAN losses for image translation and super…
Oak Ridge National Lab (ORNL)（橡树岭国家实验室）；St. Jude Children’s Hospital（圣裘德儿童医院）
Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the $z$-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this work, we introduce a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue. The proposed method combines the 2D YOLO detection method with a m…
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipelines, segmentation and label assignment are solved separately since joint optimization is computationally expensive. We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm. It optimizes an objective function closely related to the Symmetric Multiway Cut objective and empirically shows efficient scaling behavior. Due to the algorithm's eff…
Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.
Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images. This is substantiated by conducting experiments on the Lizard dataset, and through entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022, where our approach achieved the first spot on the leaderboard for the segmentation and classification task for both the preliminary and final test phase.
Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy
Aier Institute of Digital Ophthalmology and Visual Science（爱尔数字眼科学与视觉科学研究所）；Changsha Aier Eye Hospital（长沙爱尔眼科医院）；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University（复旦大学附属妇产科医院 生殖与发育研究院）
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）
Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs, on the other hand, would require manually annotated volumetric data on a large scale and hence considerable human effort. Semi-supervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more ground…
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC)（亥姆霍兹联合会马克斯·德尔布吕克分子医学中心）；Helmholtz Imaging（亥姆霍兹成像中心）；HHMI Janelia Research Campus（HHMI贾尼亚研究校区）；University of Potsdam（波茨坦大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）
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）
Friedrich Miescher Institute for Biomedical Research (FMI)（弗里德里希·米歇勒生物医学研究所（FMI））；Faculty of Sciences, University of Basel（巴塞尔大学理学院）；University of Basel（巴塞尔大学）
Instance segmentation is a key step for quantitative microscopy. While several machine learning based methods have been proposed for this problem, most of them rely on computationally complex models that are trained on surrogate tasks. Building on recent developments towards end-to-end trainable instance segmentation, we propose a minimalist recurrent network called recurrent dilated convolutional network (RDCNet), consisting of a shared stacked dilated convolution (sSDC) layer that iteratively refines its output and thereby generates interpretable intermediate predictions. It is light-weight …
Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）；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）
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become harder in the presence of noise, requiring even more high-quality training data. Hence, we propose to use denoising networks to improve the performance of other DL-based image segmentation methods. More specifically, we present ideas on how state-of-the-art sel…
Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset
Institute of Data Science and Information Computing, National Chung Hsing University（国立中兴大学数据科学与信息计算研究所）；SOE Technology Inc.（SOE科技公司）；iGroup (Asia-Pacific) Ltd.（资讯群（亚太）有限公司）
Ghent University（根特大学）；Flanders Institute for Biotechnology（佛兰德斯生物技术研究所）
Over the last decade, electron microscopy has improved up to a point that generating high quality gigavoxel sized datasets only requires a few hours. Automated image analysis, particularly image segmentation, however, has not evolved at the same pace. Even though state-of-the-art methods such as U-Net and DeepLab have improved segmentation performance substantially, the required amount of labels remains too expensive. Active learning is the subfield in machine learning that aims to mitigate this burden by selecting the samples that require labeling in a smart way. Many techniques have been pro…
Ben-Gurion University（本-古里安大学）；Kreitman School of Advanced Graduate Studies（克赖特曼高级研究生院）
We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated d…
University of Campinas（坎皮纳斯大学）；Univ Estadual Paulista（圣保罗州立大学）
Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in…
New York University（纽约大学）；Ludwigshafen Hospital（路德维希港医院）；University Hospital Bonn（波恩大学医院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort. This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of annotation. Our approach has two key aspects: (1) we construct a graph-theoretic soft-segmentation using individual seeds to be used within a regularizer during training and (2) we use an objective function that enables learning from the constructed soft-label…
Janelia Farm Research Campus, HHMI（霍华德·休斯医学研究所珍妮莉亚农场研究园区）；University of California, Riverside（加州大学河滨分校）
Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a…
The Institute of Cancer Research（癌症研究所）；University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂与华威郡大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution decon…
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Sydney Nano Institute（悉尼纳米研究所）
Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine. In this study, we propose a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework, where cell detection and segmentation are performed with a current instance segmentation pipeline and cell tracking is implemented by integrating Siamese Network with the pipeline. Besides, tracking performance is improved by incorporating spatial information into the network and fusing spatial and visual prediction. Our approach…
In this paper, we propose a new model to segment cells in phase contrast microscopy images. Cell images collected from the similar scenario share a similar background. Inspired by this, we separate cells from the background in images by formulating the problem as a low-rank and structured sparse matrix decomposition problem. Then, we propose the inverse diffraction pattern filtering method to further segment individual cells in the images. This is a deconvolution process that has a much lower computational complexity when compared to the other restoration methods. Experiments demonstrate the e…
Lehigh University（理海大学）；Hainan University（海南大学）；Tianjin University（天津大学）；Wuhan University（武汉大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This…
Polytechnique Montreal（蒙特利尔综合理工学院）；Ecole Centrale de Lille（里尔中央理工学院）；Ecole Centrale de Nantes（南特中央理工学院）；Université de Montréal（蒙特利尔大学）
Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly…
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms. Although segmentation algorithms eliminate the necessity of tracing the neurons by hand, significant manual effort is still essential for correcting the mistakes they make. A considerable amount of human labor is also required for annotating groundtruth volumes for training the classifiers of a segmentation framework. It is critically important to diminish the dependence on human interaction in the overall reconstruction system. This study proposes a novel cl…
