2D and 3D Segmentation of Uncertain Local Collagen Fiber Orientations in SHG Microscopy
Autor: | Lars Schmarje, Claus C. Glüer, Ulf Geisen, Claudius Zelenka, Reinhard Koch |
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Rok vydání: | 2019 |
Předmět: |
Artificial neural network
Computer science business.industry Fiber (mathematics) Shg microscopy Pattern recognition 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake 0302 clinical medicine Collagen fiber Fourier analysis 3d segmentation Microscopy 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Segmentation Artificial intelligence business |
Zdroj: | Lecture Notes in Computer Science ISBN: 9783030336752 GCPR |
Popis: | Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy. |
Databáze: | OpenAIRE |
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