DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction

• URL: http://arxiv.org/abs/2002.01568v1
• Date: Tue, 4 Feb 2020 22:39:58 GMT
• Title: DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction
• Authors: Leila Saadatifard, Aryan Mobiny, Pavel Govyadinov, Hien Nguyen, David Mayerich
• Abstract summary: We present a fully-convolutional, deep, and densely-connected encoder-decoder for pixel-wise semantic segmentation. The proposed network provides superior performance for semantic segmentation problems applied to open-source benchmarks.
• Score: 1.9199289015460215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maps of brain microarchitecture are important for understanding neurological function and behavior, including alterations caused by chronic conditions such as neurodegenerative disease. Techniques such as knife-edge scanning microscopy (KESM) provide the potential for whole organ imaging at sub-cellular resolution. However, multi-terabyte data sizes make manual annotation impractical and automatic segmentation challenging. Densely packed cells combined with interconnected microvascular networks are a challenge for current segmentation algorithms. The massive size of high-throughput microscopy data necessitates fast and largely unsupervised algorithms. In this paper, we investigate a fully-convolutional, deep, and densely-connected encoder-decoder for pixel-wise semantic segmentation. The excessive memory complexity often encountered with deep and dense networks is mitigated using skip connections, resulting in fewer parameters and enabling a significant performance increase over prior architectures. The proposed network provides superior performance for semantic segmentation problems applied to open-source benchmarks. We finally demonstrate our network for cellular and microvascular segmentation, enabling quantitative metrics for organ-scale neurovascular analysis.

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