SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
Institution:
1. School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom;2. School of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom;1. Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA;2. Department of Pediatrics at Boston Children’s Hospital, and Harvard Medical School, Boston, Massachusetts, USA;1. Centre for Medical Engineering, King’s College London, London, UK;2. Centre for the Developing Brain, King’s College London, London, UK;3. Department of Women and Children’s Health, King’s College London, London, UK;1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel;2. Department of Ophthalmology, Hadassah Medical Center, Jerusalem, Israel;1. Pattern Recognition Lab, DCIS, PIEAS, Nilore, Islamabad 45650, Pakistan;2. PIEAS Artificial Intelligence Center (PAIC), PIEAS, Nilore, Islamabad 45650, Pakistan;3. Deep Learning Lab, Centre for Mathematical Sciences, PIEAS, Nilore, Islamabad 45650, Pakistan;4. Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia;5. International Islamic University, Islamabad, Pakistan
Abstract:
The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customised convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei_cells_det.
