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The Kvasir-SEG Dataset

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Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation mask. Here, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. This work will be valuable for researchers to reproduce results and compare their methods in the future. By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.

Background

The human gastrointestinal tract is made up of three different sections, one of them being the large bowel. Several types of anomalies and diseases can affect the large bowel, such as colorectal cancer. Colorectal cancer is the second most common cancer type among women and third most common among men. In this respect, polyps, precursors to colorectal cancer, are important to detect early and remove. Polyps are found in nearly half of the individuals at age 50 having a screening colonoscopy, and are increasing with age. Polyps are abnormal tissue growth from the mucous membrane which is lining the inside of the GI tract, and may sometimes evolve into or be cancer. Colonoscopy is the gold standard for detection and assessment of these polyps with subsequent biopsy and removal of the polyps. Early disease detection has a huge impact on survival from colorectal cancer. In addition, several studies has shown that polyps are often overlooked during colonoscopies, with polyp miss rates of 14%-30% depending on type and size of the polyps. Increasing the detection of polyps has been shown to decrease risk of colorectal cancer. Thus, automatic detection of more polyps at an early stage can play a crucial role in improving both prevention of and survival from colorectal cancer. This is the main motivation behind development of a polyp segmentation dataset.

Original Kvasir Dataset Details

The initial Kvasir dataset (version~1) consists of 4000 images, divided into eight different classes of findings, each with 500 images. The extended Kvasir (version~2) comprises 8000 gastrointestinal (GI) tract images, each class consisting of 1000 images. These images were collected and verified by experienced gastroenterologist from Vestre Viken Health Trust in Norway. The eight classes of the dataset include anatomical landmarks, pathological findings and endoscopic procedures. Each class' images are saved in a separate folder corresponding to the class they belong to. More detailed explanation about each image class, data collection procedure and dataset details can be found on the Kvasir dataset's homepage at https://datasets.simula.no/kvasir/.

Kvasir-SEG Dataset Details

To address the high incidence of colorectal cancer, we selected the polyp class of the Kvasir dataset as the initial investigation and annotated it manually. The Kvasir-SEG dataset contains annotated polyp images and their corresponding masks. The pixels depicting polyp tissue, the region of interest, are represented by the foreground (white mask), while the background (in black) does not contain positive pixels. Some of the original images contain the image of the endoscope position marking probe, ScopeGuide TM, Olympus Tokyo Japan, located in one of the bottom corners, seen as a small green box. As this information is superfluous for the segmentation task, we have replaced these with black boxes in the new Kvasir-SEG dataset. The Kvasir_SEG_dataset (size 65 MB) archive contains 1000 polyp images and their corresponding ground truth from the Kvasir Dataset v2. The images and its corresponding masks are stored in two separate folders with the same filename. The image files are encoded using JPEG compression, and online browsing is facilitated. The open-access dataset can be easily downloaded for research and educational purposes.

Ground Truth Extraction

We uploaded the entire Kvasir polyp class to Labelbox and created all the segmentations using this application. The Labelbox is a tool used for labeling the region of interest (ROI) in image frames, i.e., the polyp regions for our case. We manually annotated and labeled all of the 1000 images with the help of medical experts. After annotation, we exported the files to generate masks for each annotation. The exported JSON file contained all the information about the image and the coordinate points for generating the mask. To create a mask, we used ROI coordinates to draw contours on an empty black image and fill the contours with white color. The generated masks are a 1-bit color depth images with a white foreground and black background.

Applications of the Dataset

The Kvasir-SEG dataset is intended to be used for researching and developing new and improved methods for segmentation, detection, localization, and classification of polyps. Multiple datasets are prerequisites for comparing computer vision-based algorithms, and this dataset is useful both as a training dataset or as a validation dataset. This dataset can assist the development of state-of-the-art solutions on images captured by colonoscopes from different manufacturers. Further research in this field has the potential to help reduce the polyp miss rate and thus improve examination quality. The Kvasir-SEG dataset is also suitable for general segmentation research. In this context, the dataset can accompany several other datasets from a wide range of fields, both medical and otherwise.

Suggested Metrics

There are different metrics for evaluating the performance of the architectures on the image segmentation dataset. For medical image segmentation task, the most commonly used ones are Dice coefficient and Intersection over Union (IOU). Based on related work in this field, we have used these metrics for the evaluation of the algorithms. In future work, we encourage the use of these metrics for evaluating the performance of the model. In the future, it might be even better to include as many as possible metrics for the fair comparison of the models.
Dice coefficient Dice coefficient is a standard metric for comparing the pixel-wise results between predicted segmentation and ground truth. It is defined as:

where A signifies the predicted set of pixels and B is the ground truth of the object to be found in the image. Here, TP represents true positive, FP represents false positive, and FN represents the false negative.
Intersection over Union (IoU) The Intersection over Union (IoU) is another standard metric to evaluate a segmentation method. The IoU calculates the similarity between predicted (A) and its corresponding ground truth (B) as shown in the equation below:

In the above equation, t is the threshold. At each threshold value t, a precision value is calculated based on the above equation and parameters, which is done by calculating the predicted object to all the ground truth objects. In addition to the above metrics, the processing speed of the system and resource consumption is of high interest.
In the above equation, t is the threshold. At each threshold value t, a precision value is calculated based on the above equation and parameters, which is done by calculating the predicted object to all the ground truth objects. In addition to the above metrics, the processing speed of the system and resource consumption is of high interest.

Use terms and citation

The use of the Kvasir-SEG dataset is restricted for research and educational purposes. The use of the Kvasir-SEG dataset for commercial purposes is forbidden without prior written permission. All the documents and publications that use Kvasir-SEG dataset or report experimental results based on the Kvasir-SEG dataset, a reference to the dataset paper have to be included. Please email debesh@simula.no if you have any questions regarding how to cite the dataset.

Download

Kvasir-SEG Dataset

The Kvasir_SEG_dataset (size 65 MB) archive contains 1000 polyp images with corresponding ground truth from the Kvasir Dataset v2. The images and corresponding masks are stored in two separate folders with the same filename. The image files are encoded using JPEG compression. The dataset can be used for developing new and improved algorithms for segmentation, detection, and classification of polyps. The open-access dataset can be easily downloaded for research and education purposes.

Contact

Email debesh (_at_) simula (_dot_) no if you have any questions about the dataset and our research activities. We always welcome collaboration and joint research!