Nuclei Detection Using Mixture Density Networks

TL;DR

This paper introduces a novel nuclei detection framework using Mixture Density Networks that effectively handles complex histology textures, shape variations, and touching cells, achieving state-of-the-art results.

Contribution

The paper proposes a new nuclei detection method based on Mixture Density Networks with a modified cost function for better handling missing nuclei and complex textures.

Findings

Achieves state-of-the-art performance on colorectal adenocarcinoma dataset

Effectively detects multiple nuclei in challenging histology images

Handles missing nuclei with a novel cost function

Abstract

Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc. This is a challenging task due to the complex texture of histology image, variation in shape, and touching cells. To tackle these hurdles, many approaches have been proposed in the literature where deep learning methods stand on top in terms of performance. Hence, in this paper, we propose a novel framework for nuclei detection based on Mixture Density Networks (MDNs). These networks are suitable to map a single input to several possible outputs and we utilize this property to detect multiple seeds in a single image patch. A new modified form of a cost function is proposed for training and handling patches with missing nuclei. The probability maps of the nuclei in the individual patches are next combined to generate the final image-wide result. The experimental results show the state-of-the-art performance on complex colorectal adenocarcinoma dataset.