IDCIA: Immunocytochemistry Dataset for Cellular Image Analysis

• URL: http://arxiv.org/abs/2411.08992v2
• Date: Tue, 19 Nov 2024 14:51:07 GMT
• Title: IDCIA: Immunocytochemistry Dataset for Cellular Image Analysis
• Authors: Abdurahman Ali Mohammed, Catherine Fonder, Donald S. Sakaguchi, Wallapak Tavanapong, Surya K. Mallapragada, Azeez Idris,
• Abstract summary: We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Our dataset includes microscopic images of cells, and for each image, the cell count and the location of individual cells.
• Score: 0.5057850174013127
• License:
• Abstract: We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Cell counting is an important step in cell analysis. Typically, domain experts manually count cells in a microscopic image. Automated cell counting can potentially eliminate this tedious, time-consuming process. However, a good, labeled dataset is required for training an accurate machine learning model. Our dataset includes microscopic images of cells, and for each image, the cell count and the location of individual cells. The data were collected as part of an ongoing study investigating the potential of electrical stimulation to modulate stem cell differentiation and possible applications for neural repair. Compared to existing publicly available datasets, our dataset has more images of cells stained with more variety of antibodies (protein components of immune responses against invaders) typically used for cell analysis. The experimental results on this dataset indicate that none of the five existing models under this study are able to achieve sufficiently accurate count to replace the manual methods. The dataset is available at https://figshare.com/articles/dataset/Dataset/21970604.

Related papers

• DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images [105.46086313858062]
  We introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.
  arXiv  Detail & Related papers  (2024-10-04T00:38:29Z)
• Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy [14.042884268397058]
  This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions.
  arXiv  Detail & Related papers  (2024-04-12T15:45:26Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Deep Learning Enabled Time-Lapse 3D Cell Analysis [7.094247258573337]
  This paper presents a method for time-lapse 3D cell analysis. We consider the problem of accurately localizing and quantitatively analyzing sub-cellular features. The code is available on Github and the method is available as a service through the BisQue portal.
  arXiv  Detail & Related papers  (2022-08-17T00:07:25Z)
• CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection [4.555723508665994]
  We propose a novel hybrid CNN-ViT model for cell detection in microscopy images. Our centroid-based cell detection method represents cells as ellipses and is end-to-end trainable.
  arXiv  Detail & Related papers  (2022-06-01T09:04:39Z)
• Learning multi-scale functional representations of proteins from single-cell microscopy data [77.34726150561087]
  We show that simple convolutional networks trained on localization classification can learn protein representations that encapsulate diverse functional information. We also propose a robust evaluation strategy to assess quality of protein representations across different scales of biological function.
  arXiv  Detail & Related papers  (2022-05-24T00:00:07Z)
• A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification [0.0]
  We build an end-to-end process to automatically segment, detect and classify cell compartments of fluorescence microscopy images of yeast cells. This fully automated process is intended to be integrated into an interactive e-Science server in the PerICo1 project. Although the application domain is optical microscopy in yeast cells, the method is also applicable to multiple-cell images in medical applications.
  arXiv  Detail & Related papers  (2021-04-06T21:24:50Z)
• Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare [77.99636165307996]
  We classify biomedical images using ensembles of neural networks. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign.
  arXiv  Detail & Related papers  (2020-11-24T01:53:39Z)
• Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training Samples [20.91256120719461]
  This work proposes a supervised learning framework to count cells from greyscale microscopic images without using annotated training images. We formulate the cell counting task as an image classification problem, where the cell counts are taken as class labels. To deal with these limitations, we propose a simple but effective data augmentation (DA) method to synthesize images for the unseen cell counts.
  arXiv  Detail & Related papers  (2020-10-28T06:19:30Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.