Sep 21 – 23, 2021
Virtual
Europe/Berlin timezone

Contour Proposal Networks for Neuronal Cell Detection

Sep 22, 2021, 3:45 PM
15m
Virtual

Virtual

Board: 09
Talk Neuro-Inspired AI Contributed Talks: Neuro-Inspired AI

Speaker

Mr Eric Upschulte (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich)

Description

For the detection of neuronal cell bodies in 1-micron BigBrain data we propose a conceptually simple framework called Contour Proposal Network (CPN). The CPN detects and segments possibly overlapping cells by fitting closed contours using a fixed-sized representation based on Fourier Descriptors. State-of-the-art object detection architectures can be used as backbone networks, forming a single-stage instance segmentation model that is trained end-to-end. We evaluate the CPN with different backbone networks using datasets from different modalities, including the 1-micron BigBrain. Experiments show that CPNs outperform U-Net and Mask R-CNN in instance segmentation accuracy. The CPN is computationally very efficient and is suitable for real-time applications when coupled with backbones such as ResNet-50 FPN. The trained models generalize well, even across different domains of cell types. The main assumption of the method regards closed object contours, hence the CPN is applicable to a wide range of detection problems also outside the biomedical domain. PyTorch code has been made available at: celldetection.org

Primary author

Mr Eric Upschulte (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich)

Co-authors

Prof. Stefan Harmeling (Institute of Computer Science, Heinrich Heine University, Düsseldorf) Prof. Katrin Amunts (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich) Dr Timo Dickscheid (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich)

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