Title:	Investigation on Encoder-Decoder Networks for Segmentation of Very Degraded X-Ray CT Tomograms
Authors:	Dulau, Idris Beurton-Aimar, Marie Hwu, Yeykuang Recur, Benoit
Citation:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 11-19.
Issue Date:	2023
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54394
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Keywords:	rentgenová nanotomografie;segmentace;hluboké učení;zobrazování mozku
Keywords in different language:	X-Ray nano-tomography;segmentation;deep-learning;brain imaging
Abstract in different language:	Field of View (FOV) Nano-CT X-Ray synchrotron imaging is used for acquiring brain neuronal features from Golgi-stained bio-samples. It theoretically requires a large number of acquired data for compensating CT recon struction noise and artefacts (both reinforced by the sparsity of brain features). However reducing the number of radiographs is essential in routine applications but it results to degraded tomograms. In such a case, traditional segmentation techniques are no longer able to distinguish neuronal structures from surrounding noise. Thus, we investigate several deep-learning networks to segment brain features from very degraded tomograms. We focus on encoder-decoder networks and define new ones addressing specifically our application. We demonstrate that some networks wildly outperform traditional segmentation and discuss the superiority of the proposed networks.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2023: Full Papers Proceedings

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