Název:	Coordinate-Unet 3D for segmentation of lung parenchyma
Autoři:	Van Linh, Le Saut, Olivier
Citace zdrojového dokumentu:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 36-42.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54397
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Klíčová slova:	segmentace plic;NSCLC;nemalobuněčný karcinom plic;koordinace;hluboké učení
Klíčová slova v dalším jazyce:	lung segmentation;NSCLC;Non-Small Cell Lung Cancer;coordinate convolutional;deep learning
Abstrakt v dalším jazyce:	Lung segmentation is an initial step to provide accurate lung parenchyma in many studies on lung diseases based on analyzing the Computed Tomography (CT) scan, especially in Non-Small Cell Lung Cancer (NSCLC) detec tion. In this work, Coordinate-UNet 3D, a model inspired by UNet, is proposed to improve the accuracy of lung segmentation in the CT scan. Like UNet, the proposed model consists of a contracting/encoder path to extract the high-level information and an expansive/decoder path to recover the features to provide the segmentation. However, we have considered modifying the structure inside each level of the model and using the Coordinate Convolutional layer as the final layer to provide the segmentation. This network was trained end-to-end by using a small set of CT scans of NSCLC patients. The experimental results show the proposed network can provide a highly accurate segmentation for the validation set with a Dice Coefficient index of 0.991, an F1 score of 0.976, and a Jaccard index (IOU) of 0.9535.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2023: Full Papers Proceedings

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