Deep Networks with Shape Priors for Nucleus Detection
This work addresses the challenge of nucleus detection in medical imaging, which is crucial for biological research and diagnostics, but it appears incremental as it builds on existing deep learning methods with added shape guidance.
The paper tackles the problem of detecting cell nuclei in microscopic images by introducing a new approach called Shape Priors with Convolutional Neural Networks (SP-CNN), which incorporates shape priors to enhance detection, resulting in competitive or superior performance compared to state-of-the-art methods on a challenging dataset.
Detection of cell nuclei in microscopic images 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 for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing. We develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN) to perform significantly enhanced nuclei detection. A set of canonical shapes is prepared with the help of a domain expert. Subsequently, we present a new network structure that can incorporate `expected behavior' of nucleus shapes via two components: {\em learnable} layers that perform the nucleus detection and a {\em fixed} processing part that guides the learning with prior information. Analytically, we formulate a new regularization term that is targeted at penalizing false positives while simultaneously encouraging detection inside cell nucleus boundary. Experimental results on a challenging dataset reveal that SP-CNN is competitive with or outperforms several state-of-the-art methods.