Jakob Dexl

Vertebral body segmentation in CT: An open dataset, deep-learning models and comparison to existing models

Felix Oliver Hofmann, Liv A Auhage, Jakob Dexl, Tengis Tschaidse et autres

PURPOSE: Vertebral bodies are anatomical landmarks for localizing measurements in applications such as body composition analysis. This study aimed to provide open-access vertebral body labels and deep-learning segmentation models, and to compare their performance in identifying the third lumbar vertebra (L3) with …

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A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions

Katharina Jeblick, Balthasar Schachtner, Andreas Mittermeier, Jakob Dexl et autres

We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Prostate-Specific Membrane Antigen (PSMA)-targeting radiotracers ([18 F]PSMA and [68Ga]Ga-PSMA-11) from 378 male patients with suspected or diagnosed prostate carcinoma. Scans were acquired between 2014 …

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HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT

Numan Saeed, Salma Hassan, Shahad Hardan, Lishan Cai et autres

Head and neck cancers (HNC) represent a significant global health burden, with accurate tumor delineation being essential for effective radiotherapy planning. The complexity of the oropharyngeal anatomy, combined with the heterogeneous appearance of tumors on imaging, makes manual segmentation time-intensive and subject …

The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization

Jakob Dexl, Katharina Jeblick, Andreas Mittermeier, Balthasar Schachtner et autres

We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generalization setting. Training data comprised 1,014 [18F]-FDG PET/CT studies from the University Hospital Tübingen and 597 [18F]/[68Ga]-PSMA PET/CT …

PSMA-FDG-PET-CT-Lesions

Sergios Gatidis, Thomas Küstner, Michael Ingrisch, Tobias Hepp et autres

Introduction A publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT studies (900 patients) and 597 prostate-specific membrane antigen (PSMA)-PET/CT studies (378 patients) acquired between 2014 and 2022 were included. The FDG cohort comprises 501 …

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PSMA-PET-CT-Lesions

Katharina Jeblick, Balthasar Maria Schachtner, Andreas Mittermeier, Jakob Dexl et autres

Introduction We provide a large, annotated dataset of 597 whole-body PSMA-PET/CT studies from 378 male patients with suspected or diagnosed prostate carcinoma to support developing and benchmarking machine learning (ML) models for automated quantitative PET/CT analysis. Alongside the FDG-PET/CT dataset, this dataset …

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Fully automated segmentation of [18F]FDG- and PSMA-PET/CT images via data-centric Deep-Learning.

Manuel Pires, Sebastian Gutschmayer, Enrica Bergalla, Stéphane Chauvie et autres

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AutoPET: Automated Lesion Segmentation in Whole-Body PET/CT

Thomas Kuestner, Sergios Gatidis, Ornela Megne, Felix Peisen et autres

Positron emission tomography (PET) combined with computed tomography (CT) plays a central role in oncologic staging, response assessment, and disease monitoring. Currently, diagnostic assessment is performed by radiologists (i.e. human observers) on PET/CT scans through the detection of changes in tumour size …

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AutoPET: Automated Lesion Segmentation in Whole-Body PET/CT

Thomas Kuestner, Sergios Gatidis, Ornela Megne, Felix Peisen et autres

Positron emission tomography (PET) combined with computed tomography (CT) plays a central role in oncologic staging, response assessment, and disease monitoring. Currently, diagnostic assessment is performed by radiologists (i.e. human observers) on PET/CT scans through the detection of changes in tumour size …

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Fully automated segmentation of [18F]FDG- and PSMA-PET/CT images via data-centric Deep-Learning.

Manuel Pires, Sebastian Gutschmayer, Enrica Bergalla, Stéphane Chauvie et autres

at, it, de, au, fr, us, fi (code pays fourni par la source)

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization

Jakob Dexl, Sergios Gatidis, Marcel Früh, Katharina Jeblick et autres

This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention in 2023. In contrast to the …

From Automation to Collaboration: The autoPET/CT-IV Challenge on Interactive Lesion Segmentation in Whole-Body PET/CT and Longitudinal CT

Pauline Ornela Megne Choudja, Jakob Dexl, Zdravko Marinov, See PDF