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Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

EJNMMI research, 2021-10, Vol.11 (1), p.105-105, Article 105

2021

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Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Scientific reports, 2021-12, Vol.11 (1), p.23905-23905, Article 23905

2021

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Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

European journal of radiology, 2019-04, Vol.113, p.89-95

2019

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

EJNMMI physics, 2020-08, Vol.7 (1), p.51-51, Article 51

2020

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Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT

Scientific reports, 2021-05, Vol.11 (1), p.10382-10382, Article 10382

2021

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Shortest Paths with Higher-Order Regularization

IEEE transactions on pattern analysis and machine intelligence, 2015-12, Vol.37 (12), p.2588-2600

2015

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Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis

Clinical imaging, 2022-01, Vol.81, p.54-59

2022

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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

European radiology experimental, 2021-03, Vol.5 (1), p.11-11, Article 11

2021

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AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

EJNMMI physics, 2021-03, Vol.8 (1), p.32-32, Article 32

2021

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Improving sensitivity through data augmentation with synthetic lymph node metastases for AI‐based analysis of PSMA PET‐CT images

Clinical physiology and functional imaging, 2024-07, Vol.44 (4), p.332-339

2024

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Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians

European journal of nuclear medicine and molecular imaging, 2022, Vol.49 (10), p.3412-3418

2022

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Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

European journal of nuclear medicine and molecular imaging, 2023-10, Vol.50 (12), p.3697-3708

2023

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Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

EJNMMI physics, 2022-02, Vol.9 (1), p.6-6, Article 6

2022

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Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma

European journal of nuclear medicine and molecular imaging, 2024, Vol.51 (8), p.2293-2307

2024

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Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

European radiology experimental, 2021-11, Vol.5 (1), p.50-50, Article 50

2021

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Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [18F]FDG PET/CT—a Retrospective Study

Nuclear medicine and molecular imaging, 2023-04, Vol.57 (2), p.110-116

2023

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AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans

Osteoporosis and Sarcopenia, 2024-06, Vol.10 (2), p.78-83

2024

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Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT

Diagnostics (Basel), 2022-08, Vol.12 (9), p.2101

2022

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A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria

Scandinavian journal of urology, 2024-01, Vol.59, p.90-97

2024

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Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

Clinical and translational radiation oncology, 2020-11, Vol.25, p.37-45

2020

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Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin lymphomas

Clinical physiology and functional imaging, 2019-01, Vol.39 (1), p.78-84

2019

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Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Clinical physiology and functional imaging, 2020-03, Vol.40 (2), p.106-113

2020

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Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

Clinical physiology and functional imaging, 2021-01, Vol.41 (1), p.62-67

2021

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Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Advances in radiation oncology, 2024-03, Vol.9 (3), p.101383-101383, Article 101383

2024

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Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

Clinical physiology and functional imaging, 2019-11, Vol.39 (6), p.399-406

2019

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Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT

Shortest Paths with Higher-Order Regularization

Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI‐based analysis of PSMA PET‐CT images

Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma

Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [18F]FDG PET/CT—a Retrospective Study

AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin lymphomas

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

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