Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Lecture notes in computer science, Год журнала: 2023, Номер unknown, С. 1 - 30

Опубликована: Янв. 1, 2023

Язык: Английский

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Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

Cancers, Год журнала: 2024, Номер 16(20), С. 3511 - 3511

Опубликована: Окт. 17, 2024

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature lymphoma makes challenging to definitively pinpoint valuable biomarkers for predicting tumor biology selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically

Язык: Английский

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Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer

Advances in Radiation Oncology, Год журнала: 2024, Номер 9(6), С. 101483 - 101483

Опубликована: Март 5, 2024

Язык: Английский

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An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images

Insights into Imaging, Год журнала: 2023, Номер 14(1)

Опубликована: Авг. 25, 2023

This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.The publicly available training dataset provided for 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well ground truth manual were input model. The data split into set 1151 cases testing 100 cases, with remaining constant throughout. Deep convolutional neural network models trained using NiftyNet platform. To test viability model, an initial reference model all followed by two additional only 575 cases. resulting predicted segmentations these then addended training.It demonstrated that approach can lead comparable gliomas (0.906 Dice score vs 0.868 score) while requiring annotation 28.6% data.The when applied drastically reduce time labor spent preparation data.Active concepts deep learning-assisted from assess their reducing required amount manually annotated training.• • gliomas. Active data.

Язык: Английский

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A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations

Physics and Imaging in Radiation Oncology, Год журнала: 2023, Номер 28, С. 100500 - 100500

Опубликована: Окт. 1, 2023

Язык: Английский

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Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning

Communications Medicine, Год журнала: 2024, Номер 4(1)

Опубликована: Июнь 8, 2024

Abstract Background Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) manually segmented with high interobserver variability. This calls reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification its downstream utilization critical. Methods Here we propose uncertainty-aware deep learning OPC GTVp segmentation, illustrate utility of multiple applications. We examine two Bayesian (BDL) models eight measures, utilize large multi-institute dataset 292 PET/CT scans to systematically analyze our approach. Results show that uncertainty-based approach accurately predicts quality segmentation 86.6% cases, identifies low performance cases semi-automated correction, visualizes regions segmentations likely fail. Conclusions Our BDL-based analysis provides first-step towards more widespread implementation segmentation.

Язык: Английский

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Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Physica Medica, Год журнала: 2023, Номер 109, С. 102568 - 102568

Опубликована: Апрель 2, 2023

Язык: Английский

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Enhancing non-small cell lung cancer radiotherapy planning: A deep learning-based multi-modal fusion approach for accurate GTV segmentation

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 92, С. 105987 - 105987

Опубликована: Фев. 8, 2024

Язык: Английский

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Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

Physics in Medicine and Biology, Год журнала: 2024, Номер 69(7), С. 075022 - 075022

Опубликована: Март 7, 2024

Abstract Objective . To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as motion-compensated simultaneous algebraic reconstruction (MC-SART) was previously developed. MC-SART employs a to obtain initial model, which suffers from lack of sufficient projections each bin. The purpose this study is demonstrate feasibility introducing model acquired during CT simulation MC-SART, coined model-based CBCT (MB-CBCT). Approach For 5 patients, we 5DCTs and pre-treatment CBCTs with breathing surrogate. We cross-calibrated 5DCT waveforms by matching diaphragms employed parameters for MC-SART. introduced Amplitude Reassignment Motion Modeling technique, measures ability control diaphragm sharpness reassigning projection amplitudes varying resolution. evaluated tumors compared them between MB-CBCT 4D-CBCT. quantified fitting error function across anatomical boundaries. Furthermore, our approach approach. MB-CBCT’s robustness over time reconstructing multiple fractions patient measuring consistency tumor centroid locations MB-CBCT. Main results found that rose consistently increasing amplitude resolution 4/5 patients. observed high image quality fractions, stable centroids average 0.74 ± 0.31 mm difference Overall, vast improvements 3D-CBCT were demonstrated terms both overall quality. Significance This work important extension technique. priori models provide compensation reconstruction. showed

Язык: Английский

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Geometric and dosimetric evaluation for breast and regional nodal auto‐segmentation structures

Journal of Applied Clinical Medical Physics, Год журнала: 2024, Номер 25(10)

Опубликована: Авг. 2, 2024

Abstract The accuracy of artificial intelligence (AI) generated contours for intact‐breast and post‐mastectomy radiotherapy plans was evaluated. Geometric dosimetric comparisons were performed between auto‐contours (ACs) manual‐contours (MCs) produced by physicians target structures. Breast regional nodal structures manually delineated on 66 breast cancer patients. ACs retrospectively generated. characteristics the breast/post‐mastectomy chestwall (CW) (axillary [AxN], supraclavicular [SC], internal mammary [IM]) geometrically evaluated Dice similarity coefficient (DSC), mean surface distance, Hausdorff Distance. also dosimetrically superimposing MC clinically delivered onto to assess impact utilizing with dose (Vx%) evaluation. Positive geometric correlations volume DSC intact‐breast, AxN, CW observed. Little or anti IM SC shown. For plans, insignificant differences MCs observed AxN V95% ( p = 0.17) 0.16), while IMN V90% significantly different. average (98.4%) (97.1%) comparable but statistically different 0.02). 0.35) 0.08) consistent MCs, Additionally, 94.1% AC‐breasts met ΔV95% variation <5% when > 0.7. However, only 62.5% AC‐CWs achieved same metrics, despite AC‐CW 0.43) being insignificant. AC structure similar MCs. may require manual adjustments. Careful review should be before treatment planning. findings this study guide clinical decision‐making process utilization AI‐driven plans. Before implementation auto‐segmentation software, an in‐depth assessment agreement each local facilities is needed.

Язык: Английский

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