387 citations · 645 across the 37 of their papers we have counts for
39 papers
AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan +13
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an over…
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp +9
Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's cl…
An automated framework for brain vessel centerline extraction from CTA images
Sijie Liu, Ruisheng Su, Jianghang Su +8
Accurate automated extraction of brain vessel centerlines from CTA images plays an important role in diagnosis and therapy of cerebrovascular diseases, such as stroke. However, thi…
AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography
Ruisheng Su, Matthijs van der Sluijs, Sandra Cornelissen +6
Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often n…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI
Bo Li, Jeroen de Bresser, Wiro Niessen +5
Lacunes of presumed vascular origin, also referred to as lacunar infarcts, are important to assess cerebral small vessel disease and cognitive diseases such as dementia. However, v…
CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography
Ruisheng Su, P. Matthijs van der Sluijs, Yuan Chen +7
Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high…
Computer-aided diagnosis and prediction in brain disorders
Vikram Venkatraghavan, Sebastian R. van der Voort, Daniel Bos +5
Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapt…
Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound
W. A. P. Bastiaansen, M. Rousian, R. P. M. Steegers-Theunissen +3
Segmentation and spatial alignment of ultrasound (US) imaging data acquired in the in first trimester are crucial for monitoring human embryonic growth and development throughout t…
An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications
Martijn P. A. Starmans, Sebastian R. van der Voort, Thomas Phil +43
Predicting clinical outcomes from medical images using quantitative features (``radiomics'') requires many method design choices, Currently, in new clinical applications, finding t…
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis
Xianjing Liu, Bo Li, Esther Bron +3
Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent t…
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
Bo Li, Wiro J. Niessen, Stefan Klein +4
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this me…
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Esther E. Bron, Stefan Klein, Janne M. Papma +14
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of…
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Bo Li, Wiro J. Niessen, Stefan Klein +3
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysi…
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos +10
Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to it…
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Sebastian R. van der Voort, Fatih Incekara, Maarten M. J. Wijnenga +14
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming tas…
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Ruisheng Su, Sandra A. P. Cornelissen, Matthijs van der Sluijs +8
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome…
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
Bo Li, Marius de Groot, Rebecca M. E. Steketee +7
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important…
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
Wietske A. P. Bastiaansen, Melek Rousian, Régine P. M. Steegers-Theunissen +3
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approac…
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
Oliver Werner, Kimberlin M. H. van Wijnen, Wiro J. Niessen +4
To measure the volume of specific image structures, a typical approach is to first segment those structures using a neural network trained on voxel-wise (strong) labels and subsequ…
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Florian Dubost, Benjamin Collery, Antonin Renaudier +5
Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose…
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Bo Li, Wiro Niessen, Stefan Klein +4
To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often in…
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
Bo Li, Marius de Groot, Meike Vernooij +3
Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there i…
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
Kimberlin M. H. van Wijnen, Florian Dubost, Pinar Yilmaz +5
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be ch…