Utilization Of Convolutional Neural Networks For Segmentation Of Mouse Embryos Cartilaginous Tissue In Micro-Ct Data

Poláková, Veronika

Utilization Of Convolutional Neural Networks For Segmentation Of Mouse Embryos Cartilaginous Tissue In Micro-Ct Data

Files

106_EEICT_2021_2.pdf(4.24 MB)

Date

2021

Authors

Poláková, Veronika

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

Automatic segmentation of the biological structures in micro-CT data is still a challengesince the object of interest (craniofacial cartilage in our case) is commonly not characterized by uniquevoxel intensity or sharp borders. In recent years, convolutional neural networks (CNNs) have becomeexceedingly popular in many areas of computer vision. Specifically, for biomedical image segmentationproblems, U-Net architecture is widely used. However, in case of micro-CT data, there isa question whether 3D CNN would not be more beneficial. This paper introduces CNN architecturebased on V-Net as well as the methodology for data preprocessing and postprocessing. The baselinearchitecture was further optimized using advanced techniques such as Atrous Spatial Pyramid Pooling(ASPP) module, Scaled Exponential Linear Unit (SELU) activation function, multi-output supervisionand Dense blocks. For network learning, modern approaches were used including learning ratewarmup or AdamW optimizer. Even though the 3D CNN do not outperform U-Net regarding the craniofacialcartilage segmentation, the optimization raises the median of Dice coefficient from 69.74 %to 80.01 %. Therefore, utilizing these advanced techniques is highly encouraged as they can be easilyadded to any U-Net-like architecture and may remarkably improve the results.

Keywords

CNN, micro-CT, image segmentation, cartilaginous tissue, V-Net

Citation

Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 106-110. ISBN 978-80-214-5943-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni

Document type

Peer-reviewed

Document version

Published version

Language of document

en

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