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Plasmodium Falciparum from Images of Giemsa for Malaria Detection Dataset

88392675
Tagmedical
Taskinstance segmentation
Release YearMade in 2019
LicenseCC BY 4.0
Download2 GB

Introduction #

Released 2019-11-11 ·Syed Saiden Abbas, Tjeerd M. H. Dijkstra

The Malaria Segmentation: A Dataset for Segmentation of Malaria Infected Cells consists of Giemsa stained images derived from blood samples of 16 patients infected with Plasmodium falciparum. Skilled experts meticulously annotated the different types of parasites within each image. The authors adopted a two-step methodology, which began with image segmentation and was followed by life stage classification. During the segmentation phase, each pixel was categorized as either a parasite or a non-parasite pixel using a random forest classifier. The performance of this segmentation process was assessed using metrics like classification accuracy, Dice coefficient, and free-response receiver operating characteristic (FROC) analysis. For the life stage classification, the segmented objects were divided into 8 classes, covering a range of parasite life stages, as well as other elements such as white blood cells or debris.

The dataset was derived from patient samples in Gambia. Plasmodium falciparum parasites were cultured ex-vivo for 24 to 48 hours. Giemsa-stained images revealed the parasites as dark purple entities, contrasting with the light pink appearance of red blood cells. Each image consisted of 4 by 4 sub-images, which were separated for individual analysis. The dataset comprised a total of 837 images obtained from 16 patients, with one patient omitted due to flawed staining.

To establish ground truth image segmentation, the authors manually selected and cropped images around dark-stained blobs containing parasites, white blood cells, or debris. Initial segmentation of parasites was performed using Otsu thresholding on the green channel, followed by manual adjustments and fine-tuning. Morphological operations such as imfill() and imdilate() in Matlab were employed for further refinement. The segmented cropped images were combined into a ground truth binary image.

image

The parasite life stages were described as follows. R = light ring, LR/ET = fat rings with dark cytoplasm, very small trophozoites with pigment; rif stage, MT = pigment, vacuole, mid-size, LT = two nuclei, very large, Esch = more than two nuclei, dark staining, very large, Lsch = still has RBC, but clear merozoites, Seg = no RBC (or very faint outline); bunch of grapes. Debris is a left-over category for those blobs that could not be clearly classified as parasites or WBCs. There were a total of 2911 parasites in different life stages, with relatively limited class imbalance. While the focus was not on WBC or debris classification, their presence was taken into account within the methodology.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkKaggle

Summary #

Plasmodium Falciparum from Images of Giemsa for Malaria Detection is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the medical industry.

The dataset consists of 883 images with 3572 labeled objects belonging to 9 different classes including Debris, Esch, R, and other: Lsch, LT, MT, LR-ET, Seg, and WBC.

Images in the Plasmodium Falciparum from Images of Giemsa for Malaria Detection dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2019 by the Radboud University Nijmegen, Netherlands and Max Planck Institute for Developmental Biology, Germany.

Dataset Poster

Explore #

Plasmodium Falciparum from Images of Giemsa for Malaria Detection dataset has 883 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 Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
OpenSample annotation mask from Plasmodium Falciparum from Images of Giemsa for Malaria DetectionSample image from Plasmodium Falciparum from Images of Giemsa for Malaria Detection
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Have a look at 883 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 9 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 1-9 of 9
Class
Images
Objects
Count on image
average
Area on image
average
Debris
mask
435
683
1.57
0.21%
Esch
mask
420
696
1.66
0.62%
R
mask
330
517
1.57
0.08%
Lsch
mask
307
439
1.43
0.62%
LT
mask
259
363
1.4
0.37%
MT
mask
243
328
1.35
0.26%
LR-ET
mask
226
303
1.34
0.13%
Seg
mask
148
200
1.35
0.6%
WBC
mask
33
43
1.3
1.09%

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 1-9 of 9
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
Esch
mask
696
0.38%
1.03%
0.03%
14px
1.36%
160px
15.53%
85px
8.27%
18px
1.3%
162px
11.72%
Debris
mask
683
0.13%
2.15%
0.02%
11px
1.07%
219px
21.26%
45px
4.39%
17px
1.23%
350px
25.33%
R
mask
517
0.05%
0.4%
0.01%
14px
1.36%
83px
8.06%
30px
2.91%
16px
1.16%
87px
6.3%
Lsch
mask
439
0.43%
1.14%
0.05%
19px
1.84%
166px
16.12%
91px
8.86%
19px
1.37%
202px
14.62%
LT
mask
363
0.26%
0.86%
0.03%
12px
1.17%
162px
15.73%
70px
6.77%
28px
2.03%
129px
9.33%
MT
mask
328
0.19%
0.58%
0.03%
25px
2.43%
125px
12.14%
60px
5.78%
21px
1.52%
106px
7.67%
LR-ET
mask
303
0.1%
0.27%
0.02%
18px
1.75%
79px
7.67%
42px
4.04%
16px
1.16%
88px
6.37%
Seg
mask
200
0.45%
1.29%
0.14%
34px
3.3%
212px
20.58%
95px
9.18%
37px
2.68%
189px
13.68%
WBC
mask
43
0.83%
2.38%
0.25%
63px
6.12%
219px
21.26%
126px
12.23%
41px
2.97%
261px
18.89%

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 3572 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 3572
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
LT
mask
Trip 808 Day 2 08-12-05 Image 1_8.png
1030 x 1382
77px
7.48%
97px
7.02%
0.39%
2
MT
mask
Trip 808 Day 2 08-12-05 Image 1_8.png
1030 x 1382
48px
4.66%
63px
4.56%
0.15%
3
LR-ET
mask
Trip 808 Day 2 08-12-05 Image 1_8.png
1030 x 1382
54px
5.24%
50px
3.62%
0.16%
4
Lsch
mask
Trip 038 Day 1 01-12-05 Image 30 add_6.png
1030 x 1382
126px
12.23%
89px
6.44%
0.61%
5
Esch
mask
Trip 038 Day 1 01-12-05 Image 30 add_6.png
1030 x 1382
96px
9.32%
81px
5.86%
0.41%
6
Esch
mask
Trip 038 Day 1 01-12-05 Image 30 add_6.png
1030 x 1382
108px
10.49%
103px
7.45%
0.58%
7
LR-ET
mask
Trip 038 Day 1 01-12-05 Image 30 add_6.png
1030 x 1382
68px
6.6%
42px
3.04%
0.15%
8
Lsch
mask
Trip 804 Day 1 02-12-05 Image 1_5.png
1030 x 1382
106px
10.29%
102px
7.38%
0.58%
9
MT
mask
Trip 804 Day 1 02-12-05 Image 1_5.png
1030 x 1382
60px
5.83%
49px
3.55%
0.13%
10
Esch
mask
Trip 804 Day 1 02-12-05 Image 1_5.png
1030 x 1382
94px
9.13%
90px
6.51%
0.44%

License #

Malaria Segmentation: A Dataset for Segmentation of Malaria Infected Cells is under CC BY 4.0 license.

Source

Citation #

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

Abbas, Syed Saiden; M. H. Dijkstra, Tjeerd (2019),
“Malaria-Detection-2019”,
Mendeley Data, V1, doi: 10.17632/5bf2kmwvfn.1

Source

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

@misc{ visualization-tools-for-malaria-segmentation-dataset,
  title = { Visualization Tools for Plasmodium Falciparum from Images of Giemsa for Malaria Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/malaria-segmentation } },
  url = { https://datasetninja.com/malaria-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Plasmodium Falciparum from Images of Giemsa for Malaria Detection 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='Plasmodium Falciparum from Images of Giemsa for Malaria Detection', 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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