Morphable Convolutional Neural Network for Biomedical Image Segmentation
Huaipan Jiang1,a, Anup Sarma1,b, Mengran Fan1,c, Jihyun Ryoo1,d, Meenakshi Arunachalam2,a Sharada Naveen2,b and Mahmut T. Kandemir1,e
1The Pennsylvania State University, University Park, PA-16802
ahzj5142@psu.edu
bavs6194@psu.edu
cmxf97@psu.edu
djihyun@psu.edu
emtk2@psu.edu
2Intel
ameena.arunachalam@intel.com
bsharada.naveen@intel.com
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.
Keywords: Image Segmentation, Approximate Computing.
