Zobrazeno 1 - 10
of 14 084
pro vyhledávání: '"Segmentation quality"'
Autor:
Qiu, Peijie, Chakrabarty, Satrajit, Nguyen, Phuc, Ghosh, Soumyendu Sekhar, Sotiras, Aristeidis
Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, p
Externí odkaz:
http://arxiv.org/abs/2412.07156
Quality control of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development. This paper introduces SegQC, a novel framework for segmentation qual
Externí odkaz:
http://arxiv.org/abs/2411.07601
Autor:
Zhang, Zheyuan, Bagci, Ulas
Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, partic
Externí odkaz:
http://arxiv.org/abs/2404.17742
Autor:
Berijanian, Maryam, Gensterblum, Katrina, Mutlu, Doruk Alp, Reagan, Katelyn, Hart, Andrew, Colbry, Dirk
This work introduces two new distance metrics for comparing labeled arrays, which are common outputs of image segmentation algorithms. Each pixel in an image is assigned a label, with binary segmentation providing only two labels ('foreground' and 'b
Externí odkaz:
http://arxiv.org/abs/2406.07851
Akademický článek
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Autor:
Maćków, Witold1 (AUTHOR) witold.mackow@zut.edu.pl, Bondarewicz, Malwina1 (AUTHOR) malwina.bondarewicz@zut.edu.pl, Łysko, Andrzej1 (AUTHOR) andrzej.lysko@zut.edu.pl, Terefenko, Paweł2 (AUTHOR) pawel.terefenko@usz.edu.pl
Publikováno v:
Remote Sensing. Sep2024, Vol. 16 Issue 17, p3344. 19p.
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates unreliable/incorrect predictions. With the introduction of the Segment
Externí odkaz:
http://arxiv.org/abs/2312.09899
Autor:
MARTINS DA CRUZ, JOSÉ-MARCIO1 Jose-Marcio.Martins@minesparis.psl.eu, SANGALLI, MATEUS1 mateussangalli@gmail.com, DECENCIÈRE, ÉTIENNE1 Etienne.Decenciere@minesparis.psl.eu, VELASCO-FORERO, SANTIAGO1 Santiago.Velasco@minesparis.psl.eu, BALDEWECK, THÉRÈSE2 Therese.Baldeweck@loreal.com
Publikováno v:
Image Analysis & Stereology. 2024, Vol. 43 Issue 2, p121-1300. 10p.
Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requi
Externí odkaz:
http://arxiv.org/abs/2303.04418
Autor:
Jose-Marcio Martins da Cruz, Mateus Sangalli, Etienne Decencière Ferrandière, Santiago Velasco-Forero, Thérèse Baldeweck
Publikováno v:
Image Analysis and Stereology, Vol 43, Iss 2 (2024)
Image segmentation is a common intermediate operation in many image processing applications. On automated systems it is important to evaluate how well it, or its subsystems are performing without access to the Ground Truth. In Deep Learning based ima
Externí odkaz:
https://doaj.org/article/0d74b5dc7592425ea5e74600d9632e6a