Morphable Convolutional Neural Network for Biomedical Image Segmentation

Huaipan Jiang, Anup Sarma, Mengran Fan, Jihyun Ryoo, Meenakshi Arunachalam, Sharada Naveen, Mahmut T. Kandemir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a morphable convolution framework, which can be applied to irregularly shaped region of input feature map. This framework reduces the computational footprint of a regular CNN operation in the context of biomedical semantic image segmentation. The traditional CNN based approach has high accuracy, but suffers from high training and inference computation costs, compared to a conventional edge detection based approach. In this work, we combine the concept of morphable convolution with the edge detection algorithms resulting in a hierarchical framework, which first detects the edges and then generate a layer-wise annotation map. The annotation map guides the convolution operation to be run only on a small, useful fraction of pixels in the feature map. We evaluate our framework on three cell tracking datasets and the experimental results indicate that our framework saves 30% and 10% execution time on CPU and GPU, respectively, without loss of accuracy, compared to the baseline conventional CNN approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1522-1525
Number of pages4
ISBN (Electronic)9783981926354
DOIs
StatePublished - Feb 1 2021
Event2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online
Duration: Feb 1 2021Feb 5 2021

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
Volume2021-February
ISSN (Print)1530-1591

Conference

Conference2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
CityVirtual, Online
Period2/1/212/5/21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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