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DeepBacs E. Coli Dataset

23581923
Tagmedical, biology
Taskobject detection
Release YearMade in 2021
LicenseCC BY 4.0
Download28 MB

Introduction #

Released 2021-10-05 ·Christoph Spahn, Romain F. Laine, Pedro Matos Pereiraet al.

The authors of the DeepBacs: Bacterial Image Analysis Using Open-source Deep Learning Approaches biomedical project highlight the rapid transformation of microscopy through Deep Learning, which has enabled efficient analysis of complex data and often surpassed the performance of classical algorithms. As a result, there has been a significant effort to develop user-friendly tools that allow biomedical researchers, with limited computer science backgrounds, to leverage DL effectively. While such approaches have primarily focused on analyzing microscopy images from eukaryotic samples, they have been underutilized in microbiology.

To support researchers in their training process, the authors provide a specifically designed database of training and testing data, allowing bacteriologists to quickly explore how to analyze their data using DL techniques. The ultimate goal is to foster the efficient application of DL in microbiology, leading to the creation of novel tools for bacterial cell biology and antibiotic research.

The presented dataset contains training and test images of E. coli cells treated with different antibiotics for antibiotic phenotyping:

Characteristic Value
Data type Paired microscopy images (confocal fluorescence) and manual annotations
Microscopy data type Confocal fluorescence images of fixed E. coli cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format
Microscope Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA)
Cell type Chemically fixed E. coli NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated);
Image size 400 x 400 px² (Pixel size: 84 nm)
ExpandExpand
Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

DeepBacs: Escherichia Coli Antibiotic Phenotyping Object Detection Dataset and YOLOv2 Model is a dataset for an object detection task. It is used in the biomedical research.

The dataset consists of 235 images with 3564 labeled objects belonging to 8 different classes including Oblique, Vesicle, Nalidixate, and other: Rifampicin, CAM, Control, Mecillinam, and MP265.

Images in the DeepBacs E. Coli dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (153 images), test_individual_treatment (50 images), and test_stitched_images (32 images). The dataset was released in 2021 by the Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Germany.

Here is the visualized example grid with annotations:

Explore #

DeepBacs E. Coli dataset has 235 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
OpenSample annotation mask from DeepBacs E. ColiSample image from DeepBacs E. Coli
👀
Have a look at 235 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 8 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-8 of 8
Class
Images
Objects
Count on image
average
Area on image
average
Oblique
rectangle
201
789
3.93
2.98%
Vesicle
rectangle
100
393
3.93
0.45%
Nalidixate
rectangle
64
209
3.27
4.64%
Rifampicin
rectangle
56
597
10.66
8.82%
CAM
rectangle
52
374
7.19
5.82%
Control
rectangle
48
308
6.42
7.88%
Mecillinam
rectangle
47
484
10.3
6.73%
MP265
rectangle
37
410
11.08
6.5%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-8 of 8
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
Oblique
rectangle
789
0.76%
4.84%
0.05%
10px
2.5%
106px
26.5%
35px
8.66%
8px
2%
97px
24.25%
Rifampicin
rectangle
597
0.85%
2.21%
0.32%
18px
4.5%
71px
17.75%
36px
9.07%
16px
4%
83px
20.75%
Mecillinam
rectangle
484
0.66%
1.86%
0.26%
19px
4.75%
66px
16.5%
33px
8.2%
18px
4.5%
65px
16.25%
MP265
rectangle
410
0.59%
1.55%
0.28%
18px
4.5%
58px
14.5%
31px
7.7%
17px
4.25%
64px
16%
Vesicle
rectangle
393
0.12%
0.26%
0.05%
8px
2%
22px
5.5%
14px
3.42%
9px
2.25%
21px
5.25%
CAM
rectangle
374
0.81%
2.36%
0.07%
10px
2.5%
86px
21.5%
37px
9.17%
10px
2.5%
83px
20.75%
Control
rectangle
308
1.24%
3.26%
0.42%
19px
4.75%
101px
25.25%
48px
11.93%
18px
4.5%
96px
24%
Nalidixate
rectangle
209
1.43%
3.49%
0.46%
21px
5.25%
89px
22.25%
50px
12.59%
16px
4%
122px
30.5%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 3564 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 3564
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
Oblique
rectangle
#6_translated_control_1.png
400 x 400
21px
5.25%
37px
9.25%
0.49%
2
Oblique
rectangle
#6_translated_control_1.png
400 x 400
33px
8.25%
46px
11.5%
0.95%
3
Oblique
rectangle
#6_translated_control_1.png
400 x 400
35px
8.75%
38px
9.5%
0.83%
4
Oblique
rectangle
#6_translated_control_1.png
400 x 400
21px
5.25%
63px
15.75%
0.83%
5
Oblique
rectangle
#6_translated_control_1.png
400 x 400
42px
10.5%
38px
9.5%
1%
6
Oblique
rectangle
#6_translated_control_1.png
400 x 400
41px
10.25%
29px
7.25%
0.74%
7
Oblique
rectangle
#6_translated_control_1.png
400 x 400
39px
9.75%
46px
11.5%
1.12%
8
Oblique
rectangle
#6_translated_control_1.png
400 x 400
26px
6.5%
34px
8.5%
0.55%
9
Oblique
rectangle
#6_translated_control_1.png
400 x 400
44px
11%
37px
9.25%
1.02%
10
Oblique
rectangle
#6_translated_control_1.png
400 x 400
46px
11.5%
22px
5.5%
0.63%

License #

DeepBacs: Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model is under CC BY 4.0 license.

Source

Citation #

If you make use of the DeepBacs data, please cite the following reference:

@dataset{spahn_christoph_2021_5551057,
  author       = {Spahn, Christoph and
                  Heilemann, Mike},
  title        = {{DeepBacs – Escherichia coli antibiotic phenotyping 
                   object detection dataset and YOLOv2 model}},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.5551057},
  url          = {https://doi.org/10.5281/zenodo.5551057}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-deepbacs-dataset,
  title = { Visualization Tools for DeepBacs E. Coli Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/deepbacs } },
  url = { https://datasetninja.com/deepbacs },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset DeepBacs E. Coli can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='DeepBacs E. Coli', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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