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Wiro J. Niessen
Delft University of Technology
Deep learningPattern recognitionComputer scienceConvolutional neural networkSegmentation
56Publications
7H-index
156Citations
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Publications 60
Newest
#1Florian Dubost (Harvard University)H-Index: 4
#2Marleen de Bruijne (UCPH: University of Copenhagen)H-Index: 30
Last. Wiro J. Niessen (TU Delft: Delft University of Technology)H-Index: 7
view all 13 authors...
Abstract Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan usin...
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#1Oliver Werner (EUR: Erasmus University Rotterdam)
Last. Marleen de BruijneH-Index: 30
view all 7 authors...
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 subsequently compute the volume from the segmentation. A more straightforward approach would be to predict the volume directly using a neural network based regression approach, trained on image-level (weak) labels indicating volume. In this article, we compared networks optimized with weak and strong labels, and study their a...
#1Francesca PizziniH-Index: 21
#2Filippo Pesapane (University of Milan)H-Index: 6
Last. Nils Broeckx (University of Antwerp)H-Index: 1
view all 5 authors...
3 CitationsSource
#1Michael P. RechtH-Index: 36
#2Marc DeweyH-Index: 40
Last. John J. SmithH-Index: 1
view all 7 authors...
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles fo...
1 CitationsSource
#2M.P.A. StarmansH-Index: 1
Last. Stefan KleinH-Index: 31
view all 11 authors...
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#1M.P.A. Starmans (EUR: Erasmus University Rotterdam)H-Index: 1
#2Sebastian R. van der Voort (EUR: Erasmus University Rotterdam)H-Index: 1
Last. Wiro J. Niessen (TU Delft: Delft University of Technology)H-Index: 7
view all 6 authors...
Abstract Radiomics uses multiple image features from medical imaging data to predict clinical variables. Various features can be constructed to describe the properties of the full image, or those of a specific region of interest such as a tumor. These features may be related to a wide variety of clinical variables, such as disease characteristics, genetics, and therapy response. This can be done through the use of machine learning, which enables the training of a model on these features using da...
1 CitationsSource
#2Fatih IncekaraH-Index: 1
Last. Marion SmitsH-Index: 33
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Purpose: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI. Experimental Design: Preoperative brain MR images from 284 patients who had undergone b...
2 CitationsSource
#1Sven J. van der Lee (EUR: Erasmus University Rotterdam)H-Index: 24
#2Maria J. Knol (EUR: Erasmus University Rotterdam)H-Index: 3
Last. Charles D. Smith (UC Davis: University of California, Davis)H-Index: 122
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Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocamp...
1 CitationsSource
#1Elisabeth J. Vinke (EUR: Erasmus University Rotterdam)H-Index: 1
#2Wyke Huizinga (EUR: Erasmus University Rotterdam)H-Index: 5
view all 12 authors...
textabstractBrain imaging data are increasingly made publicly accessible and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient’s base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure vo...
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#1Margreet C. Vos (EUR: Erasmus University Rotterdam)H-Index: 26
#2M.P.A. Starmans (EUR: Erasmus University Rotterdam)H-Index: 1
Last. Cornelis Verhoef (EUR: Erasmus University Rotterdam)H-Index: 42
view all 13 authors...
Background: Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI. Methods: Patients with an MDM2-negative lipoma or MDM2-positive WDLPS and a pretreatment T1-weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were ...
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