Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to arbitrariness due to the operator's interpretation of the borderline cases.
We propose a Machine Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items.
Specifically, we adopt a UNet-like architecture leveraging residual units and an extended bottleneck for enlarging the field-of-view. In addition, we make use of weighted maps that penalize the errors on cells boundaries increasingly with overcrowding. These changes provide more context and force the model to focus on relevant features during pixel-wise classification. As a result, the model performance is enhanced, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior assessment of the results with domain experts confirms that the model detects cells of interest correctly. The model demonstrates a human-level ability inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. This qualitative assessment is also corroborated by quantitative metrics as an ${F_1}$ score of 0.87.
Despite some difficulties in interpretation, results are also satisfactory with respect to the counting task, as testified by mean and median absolute error of, respectively, 0.8 and 1.
New York University（纽约大学）；University Hospital Bonn（波恩大学医院）
Object counting and localization are key steps for quantitative analysis in large-scale microscopy applications. This procedure becomes challenging when target objects are overlapping, are densely clustered, and/or present fuzzy boundaries. Previous methods producing density maps based on deep learning have reached a high level of accuracy for object counting by assuming that object counting is equivalent to the integration of the density map. However, this model fails when objects show significant overlap regarding accurate localization. We propose an alternative method to count and localize …
Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative feedback that drive research. Unfortunately, cell counting is most commonly a manual task and can be time-intensive. The task is made even more difficult due to overlapping cells, existence of multiple focal planes, and poor imaging quality, among other factors. Here, we describe a convolutional neural network approach, using a recently described feature pyramid network combined with a VGG-style neural network, for …
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle Foundation Hospital（卡尔基金会医院）
Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to othe…
Istituto Sistemi e Electronica Applicata (ISEA)（应用系统与电子研究所）；Scuola universitaria professionale della Svizzera italiana (SUPSI)（瑞士意大利语区应用科技大学）；Grupo de investigacion SISTEMIC（SISTEMIC研究组）；Facultad de Ingenieria, Universidad de Antioquia UdeA（安蒂奥基亚大学工程学院）
An automatic approach to counting any kind of cells could alleviate work of the experts and boost the research in fields such as regenerative medicine. In this paper, a method for microscopy cell counting using multiple frames (hence temporal information) is proposed. Unlike previous approaches where the cell counting is done independently in each frame (static cell counting), in this work the cell counting prediction is done using multiple frames (dynamic cell counting). A spatiotemporal model using ConvNets and long short term memory (LSTM) recurrent neural networks is proposed to overcome t…
State University of New York at Stony Brook（纽约州立大学石溪分校）
Microscopy images from different imaging conditions, organs, and tissues often have numerous cells with various shapes on a range of backgrounds. As a result, designing a deep learning model to count cells in a source domain becomes precarious when transferring them to a new target domain. To address this issue, manual annotation costs are typically the norm when training deep learning-based cell counting models across different domains. In this paper, we propose a cross-domain cell counting approach that requires only weak human annotation efforts. Initially, we implement a cell counting netw…
Institute of Physics of the Czech Academy of Sciences, v.v.i.（捷克科学院物理研究所）；Regional Centre of Advanced Technologies and Materials, Palacký University（帕拉茨基大学先进技术与材料区域中心）；RWTH Aachen University（亚琛工业大学）
Scanning tunneling and atomic force microscopies (STM/nc-AFM) are rapidly progressing to offer unprecedented spatial resolution of a diverse array of chemical species. In particular, they are employed to characterize on-surface chemical reactions by directly examining precursors and products. Chiral effects and self-assembled structures can also be investigated. This open source, modular, python based scheme automates the categorization of a variety of molecules present in medium sized (10$\times$10 to 100$\times$100 nm) scanned probe images.
CellFMCount: A Fluorescence Microscopy Dataset, Benchmark, and Methods for Cell Counting
Department of Computer Science（计算机科学系）；Department of Genetics, Development, and Cell Biology（遗传学、发育与细胞生物学系）；Molecular, Cellular, and Developmental Biology Program（分子、细胞与发育生物学项目）；Neuroscience Program（神经科学项目）；Nanovaccine Institute（纳米疫苗研究所）；Iowa State University（爱荷华州立大学）
Faculdade de Ciências da Universidade do Porto（波尔图大学理学院）
Analysis of microscopy images is one important tool in many fields of biomedical research, as it allows the quantification of a multitude of parameters at the cellular level. However, manual counting of these images is both tiring and unreliable and ultimately very time-consuming for biomedical researchers. Not only does this slow down the overall research process, it also introduces counting errors due to a lack of objectivity and consistency inherent to the researchers own human nature. This thesis addresses this issue by automatically determining infection indexes of macrophages parasite by…
Dr. A.P.J. Abdul Kalam Technical University（阿卜杜勒·卡拉姆技术大学）
Every year millions of people die due to disease of Cancer. Due to its invasive nature it is very complex to cure even in primary stages. Hence, only method to survive this disease completely is via forecasting by analyzing the early mutation in cells of the patient biopsy. Cell Segmentation can be used to find cell which have left their nuclei. This enables faster cure and high rate of survival. Cell counting is a hard, yet tedious task that would greatly benefit from automation. To accomplish this task, segmentation of cells need to be accurate. In this paper, we have improved the learning o…
Oxford Brookes University（牛津布鲁克斯大学）；Vellore Institute of Technology（韦洛尔理工学院）
Tuberculosis is a contagious disease which is one of the leading causes of death, globally. The general diagnosis methods for tuberculosis include microscopic examination, tuberculin skin test, culture method, enzyme linked immunosorbent assay (ELISA) and electronic nose system. World Health Organization (WHO) recommends standard microscopic examination for early diagnosis of tuberculosis. In microscopy, the technician examines field of views (FOVs) in sputum smear for presence of any TB bacilli and counts the number of TB bacilli per FOV to report the level of severity. This process is time c…
Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer. ccRCC originates from the epithelial lining of proximal convoluted renal tubules. These cells undergo abnormal mutations in the presence of Ki67 protein and create a lump-like structure through cell proliferation. Manual counting of tumor cells in the tissue-affected sections is one of the strongest prognostic markers for renal cancer. However, this procedure is time-consuming and also prone to subjectivity. These assessments are based on the physical cell appeara…
School of Computer Science and Engineering（计算机科学与工程学院）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
The need to count and localize repeating objects in an image arises in different scenarios, such as biological microscopy studies, production lines inspection, and surveillance recordings analysis. The use of supervised Convoutional Neural Networks (CNNs) achieves accurate object detection when trained over large class-specific datasets. The labeling effort in this approach does not pay-off when the counting is required over few images of a unique object class. We present a new method for counting and localizing repeating objects in single-image scenarios, assuming no pre-trained classifier is…
University of Tennessee Health Science Center（田纳西大学健康科学中心）
The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples…
MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions
School of Computing and Communications（计算与通信学院）；Lancaster University（兰卡斯特大学）；Department of Cell Biology（细胞生物学系）；Harvard Medical School（哈佛医学院）；Department of Molecular and Cellular Biology（分子与细胞生物学系）；Harvard University（哈佛大学）
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
National Institute of Nuclear Physics（国家核物理研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Bologna（博洛尼亚大学）
Tsinghua University（清华大学）；China University of Geosciences, Wuhan（武汉地质大学）；State Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）
Automated Palynological Analysis System: Integrating Deep Metric Learning, Detection and Classification in Bright Field Microscopy
Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción（物理系，物理与数学科学学院，康塞普西翁大学）；Facultad de Ingeniería, Universidad San Sebastián（工程学院，圣塞巴斯蒂安大学）；Facultad de Ingeniería, Universidad Andres Bello（工程学院，安德烈斯·贝洛大学）；Universidad Arturo Prat, Sede Victoria（阿尔托普大学，维多利亚校区）；Laboratory of Palynology and Plant Ecology, School of Sciences and Technologies, University of Concepción（古生物与植物生态实验室，科学与技术学院，康塞普西翁大学）
Beihang University（北京航空航天大学）；Peking University First Hospital（北京大学第一医院）；Peking University（北京大学）
Accurately and quickly binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual microscopy counting is time-consuming and lacks objectivity. Moreover, with the limitation of staining quality and diversity of morphology features in BC microscopy whole slide images (WSIs), traditional image processing approaches are helpless. To overcome this challenge, we propose a two-stage detection method inspired by the structure prior of BC based on deep learning, which cascades to implement BCs coarse detection at the WSI-level a…
Massachusetts Institute of Technology（麻省理工学院）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Universidade de São Paulo（圣保罗大学）
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data. We apply for the first time an object detection model previously used on natural images to identify cells and recognize their stages in brightfield microscopy images of malaria-infected blood. Many micro-organisms like malaria parasites are still studied by expert manual inspection and hand counting. This type of object detection task is challenging due to factors like variations in cell shape, density, and color, and uncertainty of s…
Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Technical University of Munich（慕尼黑工业大学）；TUMCREATE；Bioinformatics Institute, Agency for Science, Technology and Research（科技研究局生物信息研究所）；TUM University Hospital（TUM大学医院）；National University of Singapore（新加坡国立大学）；National University Hospital（新加坡国立大学医院）；German Center for Infection Research (DZIF)（德国感染研究中心）；Infectious Diseases Labs (AIDL), Agency for Science, Technology and Research（科技研究局传染病实验室）；Nanyang Technological University（南洋理工大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research（科技研究局前沿人工智能研究中心）
