Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images

• URL: http://arxiv.org/abs/2011.03683v2
• Date: Tue, 10 Nov 2020 01:57:30 GMT
• Title: Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
• Authors: Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio and Hua Li
• Abstract summary: We propose a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art regression-based methods. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.
• Score: 9.392002197101965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 other state-of-the-art density regression-based methods. First, the density regression model (DRM) is designed as a concatenated fully convolutional regression network (C-FCRN) to employ multi-scale image features for the estimation of cell density maps from given images. Second, auxiliary convolutional neural networks (AuxCNNs) are employed to assist in the training of intermediate layers of the designed C-FCRN to improve the DRM performance on unseen datasets. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.

Related papers

• 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)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• 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)
• Increasing a microscope's effective field of view via overlapped imaging and machine learning [4.23935174235373]
  This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis.
  arXiv  Detail & Related papers  (2021-10-10T22:52:36Z)
• From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research [0.0]
  Cell image analysis is crucial in Alzheimer's research to detect the presence of A$beta$ protein inhibiting cell function. We first found Unet is most suitable in augmented microscopy by comparing performance in multi-class semantics segmentation. We develop the augmented microscopy method to capture nuclei in a brightfield image and the transformer using Unet model to convert an input image into a sequence of topological information.
  arXiv  Detail & Related papers  (2021-08-03T16:59:33Z)
• 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)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection [6.836162272841265]
  We introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. We observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8%$ on average.
  arXiv  Detail & Related papers  (2020-05-19T11:31:53Z)

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.