MiDeSeC: A Dataset for Mitosis Detection and Segmentation in Breast Cancer Histopathology Images

TL;DR

This paper introduces MiDeSeC, a comprehensive dataset of breast cancer histopathology images with annotated mitoses, aimed at advancing detection and segmentation methods in medical imaging research.

Contribution

The paper presents a new, large-scale dataset for mitosis detection and segmentation in breast cancer histopathology images, covering diverse mitosis shapes and providing training and testing splits.

Findings

Over 500 mitoses annotated in the dataset

Includes images from 25 patients with diverse mitosis appearances

Dataset facilitates development of automated detection algorithms

Abstract

The MiDeSeC dataset is created through H&E stained invasive breast carcinoma, no special type (NST) slides of 25 different patients captured at 40x magnification from the Department of Medical Pathology at Ankara University. The slides have been scanned by 3D Histech Panoramic p250 Flash-3 scanner and Olympus BX50 microscope. As several possible mitosis shapes exist, it is crucial to have a large dataset to cover all the cases. Accordingly, a total of 50 regions is selected from glass slides for 25 patients, each of regions with a size of 1024*1024 pixels. There are more than 500 mitoses in total in these 50 regions. Two-thirds of the regions are reserved for training, the other third for testing.