Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool DOI Creative Commons

Maria Galanty,

Dieuwertje Luitse, Sijm H. Noteboom

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 30, 2024

Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases these on which deep-learning models trained can compromise reliability. This study investigates stemming from dataset-creation practices. Drawing existing guidelines, we first developed a BEAMRAD tool to assess the documentation of public Magnetic Resonance Imaging (MRI); Color Fundus Photography (CFP), and Electrocardiogram (ECG) datasets. In doing so, provide an overview that may emerge due inadequate dataset documentation. Second, examine current state medical images signal data. Our research reveals there is substantial variance image datasets, even though guidelines have been imaging. indicates subject individual discretionary decisions. Furthermore, find aspects such as hardware data acquisition details commonly documented, while information regarding annotation practices, error quantification, or limitations not consistently reported. risks having considerable implications abilities users detect potential sources bias through respective develop reliable robust be adapted clinical practice.

Язык: Английский

Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning DOI
SA Yoganathan, Tarraf Torfeh, Satheesh Paloor

и другие.

Biomedical Physics & Engineering Express, Год журнала: 2025, Номер 11(2), С. 025007 - 025007

Опубликована: Янв. 17, 2025

Abstract Background and Purpose : This study aimed to develop evaluate an efficient method automatically segment T1- T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against ensemble approach advance accuracy MRI-guided radiotherapy (RT) planning. Materials Methods . The evaluation was conducted on a private clinical dataset publicly available (HaN-Seg). Anonymized MRI data from 55 cancer patients, including T1-weighted, T1-weighted with contrast, images, were used in dataset. employed EDL strategy that integrated five independently trained 2D networks, each tailored for precise tumors organs at risk (OARs) scans. Class probabilities obtained by averaging final layer activations (Softmax outputs) networks using weighted-average method, which then converted into discrete labels. Segmentation evaluated Dice similarity coefficient (DSC) Hausdorff distance 95% (HD95). model also tested HaN-Seg public comparison. Results demonstrated superior both datasets. For dataset, achieved average DSC 0.7 ± 0.2 HD95 4.5 2.5 mm across all segmentations, significantly outperforming yielded values ≤0.6 ≥14 mm. Similar improvements observed Conclusions Our shows consistently outperforms CNN datasets, demonstrating potential learning enhance accuracy. These findings underscore value applications, particularly RT

Язык: Английский

Процитировано

0

A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer DOI Creative Commons
E. Mastella, Francesca Calderoni, Luigi Manco

и другие.

Physics and Imaging in Radiation Oncology, Год журнала: 2025, Номер 33, С. 100731 - 100731

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Rep-MedSAM: Towards Real-Time and Universal Medical Image Segmentation DOI
Mu-Xin Wei, Shuqing Chen,

Silin Wu

и другие.

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 57 - 69

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy DOI Creative Commons

Jessica Kächele,

Maximilian Zenk, Maximilian Rokuss

и другие.

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 50 - 64

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance Images: Contribution to the HNTSMRG Challenge DOI Creative Commons
Marek Wodziński

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 204 - 213

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Generiranje sintetičnih CT slik iz MR slik področja glave in vratu z uporabo difuzijskih modelov DOI Creative Commons

Rok Marko Šter,

Gašper Podobnik, Tomaž Vrtovec

и другие.

Опубликована: Март 7, 2025

Računalniška tomografija (CT) je slikovna preiskava, ki se v klinični praksi standardno zajame okviru načrtovanje radioterapije. V primeru raka območju glave in vratu (HaN) pogosto tudi magnetno resonančne (MR) slike za natančnejše orisovanje tumorjev kritičnih organov. zadnjem času vse bolj uveljavlja radioterapija na podlagi MR-samostojnega pristopa, odstrani potrebo po zajemu CT slik s tem izpostavljenost ionizirajočemu sevanju, vendar pa zahteva rešitev generiranje sintetičnih MR . Nedavne študije kažejo, da difuzijski modeli nudijo realistično z natančnimi anatomskimi podrobnostmi manj artefakti kot generativne nasprotniške mreže. tej študiji smo razvili model pretvorbo sintetične HaN področje. Naš pristop, ovrednoten zbirki podatkov HaN-Seg, vključuje pare istih bolnikov, doseže indeks strukturne podobnosti 92,2 %, vršno razmerje signal-šum 33,1 dB ter povprečno absolutno napako 35,3 HU. Model dodatno ovrednotimo segmentacijo Rezultati potrjujejo potencial uporabe difuzijskih modelov pri načrtovanju

Процитировано

0

Towards fully automatized [177Lu]Lu-PSMA personalized dosimetry based on 360° CZT whole-body SPECT/CT: a proof-of-concept DOI Creative Commons
Arnaud Dieudonné,

A. Terro,

Arthur Dumouchel

и другие.

EJNMMI Physics, Год журнала: 2025, Номер 12(1)

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

0

Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation DOI
Kamil Kwarciak, Mateusz Danioł, Daria Hemmerling

и другие.

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 165 - 179

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Fast MRI reconstruction: A thorough survey from single-modal to multi-modal DOI

Weiyi Lyu,

Xinming Fang, Chaoyan Huang

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127703 - 127703

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Virtual monochromatic image-based automatic segmentation strategy using deep learning method DOI
Li Chen,

Shutong Yu,

Yan Chen

и другие.

Physica Medica, Год журнала: 2025, Номер 134, С. 104986 - 104986

Опубликована: Май 2, 2025

Язык: Английский

Процитировано

0