The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures

• URL: http://arxiv.org/abs/2308.12116v1
• Date: Wed, 23 Aug 2023 13:10:33 GMT
• Title: The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures
• Authors: Christoph Reich, Tim Prangemeier, Heinz Koeppl
• Abstract summary: trapped yeast cell (TYC) dataset is a novel dataset for understanding instance-level semantics and motions of cells in microstructures. TYC offers ten times more instance annotations than the previously largest dataset, including cells and microstructures.
• Score: 29.29348484938194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting cells and tracking their motion over time is a common task in biomedical applications. However, predicting accurate instance-wise segmentation and cell motions from microscopy imagery remains a challenging task. Using microstructured environments for analyzing single cells in a constant flow of media adds additional complexity. While large-scale labeled microscopy datasets are available, we are not aware of any large-scale dataset, including both cells and microstructures. In this paper, we introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release $105$ dense annotated high-resolution brightfield microscopy images, including about $19$k instance masks. We also release $261$ curated video clips composed of $1293$ high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology. TYC offers ten times more instance annotations than the previously largest dataset, including cells and microstructures. Our effort also exceeds previous attempts in terms of microstructure variability, resolution, complexity, and capturing device (microscopy) variability. We facilitate a unified comparison on our novel dataset by introducing a standardized evaluation strategy. TYC and evaluation code are publicly available under CC BY 4.0 license.

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)
• Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology [2.7280901660033643]
  This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. We develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time.
  arXiv  Detail & Related papers  (2024-04-16T02:42:06Z)
• The Berkeley Single Cell Computational Microscopy (BSCCM) Dataset [1.53744306569115]
  We introduce the Berkeley Single Cell Computational Microscopy dataset. This dataset contains over 12,000,000 images of 400,000 of individual white blood cells.
  arXiv  Detail & Related papers  (2024-02-09T05:10:53Z)
• 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)
• An Instance Segmentation Dataset of Yeast Cells in Microstructures [26.801504820020288]
  This paper presents a novel dataset for segmenting yeast cells in microstructures. In total, we release 493 densely annotated microscopy images. The aim of the dataset and evaluation strategy is to facilitate the development of new cell segmentation approaches.
  arXiv  Detail & Related papers  (2023-04-15T17:05:24Z)
• 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)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning [20.456742449675904]
  We present convolutional neural networks trained for multiclass segmenting of individual yeast cells. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application.
  arXiv  Detail & Related papers  (2020-11-16T16:16:13Z)
• 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)
• Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation [71.50526869670716]
  We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
  arXiv  Detail & Related papers  (2020-07-21T14:05:09Z)
• 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.