# Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection

## TL;DR

This paper introduces a novel deep learning approach for cell nucleus detection that incorporates shape priors through regularization and trainable layers, improving accuracy over existing methods.

## Contribution

It develops a new CNN architecture with shape prior integration and trainable components, enhancing nucleus detection by leveraging domain expert knowledge.

## Key findings

- Outperforms state-of-the-art methods on challenging datasets
- Effectively incorporates shape priors into deep learning models
- Reduces false positives and improves detection accuracy

## Abstract

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train Convolutional Neural Networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many such methods are supplemented by spatial or morphological processing. Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN). We further extend the network to introduce a shape prior (SP) layer and then allowing it to become trainable (i.e. optimizable). We call this network tunable SP-CNN (TSP-CNN). In summary, we present new network structures that can incorporate 'expected behavior' of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information. Analytically, we formulate two new regularization terms that are targeted at: 1) learning the shapes, 2) reducing false positives while simultaneously encouraging detection inside the cell nucleus boundary. Experimental results on two challenging datasets reveal that the proposed SP-CNN and TSP-CNN can outperform state-of-the-art alternatives.