@inproceedings{291dfbcb94c546a29c16d30aeddcc35d,
title = "Biomedical Image Segmentation Using Fully Convolutional Networks on TrueNorth",
abstract = "With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor - TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.",
keywords = "fcn, glial cell, IBM, neural network, segmentation, truenorth",
author = "Indranil Palit and Lin Yang and Yue Ma and Danny Chen and Michael Niemier and Jinjun Xiong and Hu, \{X. Sharon\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 ; Conference date: 18-06-2018 Through 21-06-2018",
year = "2018",
month = jul,
day = "20",
doi = "10.1109/CBMS.2018.00072",
language = "English",
series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "375--380",
editor = "Bridget Kane and Jaakko Hollmen and Carolyn McGregor and Paolo Soda",
booktitle = "Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018",
address = "United States",
}