Název:	Investigation on Encoder-Decoder Networks for Segmentation of Very Degraded X-Ray CT Tomograms
Autoři:	Dulau, Idris Beurton-Aimar, Marie Hwu, Yeykuang Recur, Benoit
Citace zdrojového dokumentu:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 11-19.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54394
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Klíčová slova:	rentgenová nanotomografie;segmentace;hluboké učení;zobrazování mozku
Klíčová slova v dalším jazyce:	X-Ray nano-tomography;segmentation;deep-learning;brain imaging
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2023: Full Papers Proceedings

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