"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:f970b944-7912-4843-a1e5-1d55008d4a90","http://resolver.tudelft.nl/uuid:f970b944-7912-4843-a1e5-1d55008d4a90","Classification of primary liver tumors with radiomics and deep learning based on multiphasic MRI","Goedhart, Aisha (TU Delft Mechanical, Maritime and Materials Engineering)","Vos, F.M. (graduation committee); Starmans, M. P.A. (mentor); Klein, Stefan (graduation committee); Delft University of Technology (degree granting institution)","2023","Primary liver cancer is a commonly diagnosed cancer and accurate diagnosis is crucial for treatment planning. To differentiate between malignant and benign liver tumors, contrast-enhanced MRI is typically used as it provides information over multiple contrast phases. However, diagnosis based on MRI is challenging. In this study, automatic classification is used to distinguish common primary liver tumors.
Imaging data from 102 patients with malignant (hepatocellular carcinoma) and benign (focal nodular hyperplasia and hepatocellular adenoma) primary liver tumors was used for binary classification through radiomics and deep learning approaches. The radiomics method was applied with the use of the open-source toolbox WORC. The deep learning model was based on the ResNet-10 architecture. The data input consisted of individual and combined phases of contrast-enhanced T1-weighted and T2-weighted MRI.
The highest performance values were found for the radiomics approach that combined the precontrast, arterial, portal venous, and delayed contrast phases together with T2-weighted MRI, with an AUC of 0.92. The deep learning model scored an AUC of 0.83 with this data input, however substantial overfitting occurred due to the limited sample size.
In conclusion, the radiomics classifiers based on combined contrast-enhanced T1-weighted and T2-weighted MRI can differentiate malignant from benign primary liver tumors with limited data samples. The classification task is too complex with the given data when using a ResNet-10 model and should be applied to an extended dataset.","Radiomics; Deep Learning; ResNet; MRI; Post-contrast T1; Liver cancer; Machine Learning","en","master thesis","","","","","","","","","","","","Biomedical Engineering | Medical Physics","",""
"uuid:e8110167-840f-43d0-8336-3c7d45e7f3e4","http://resolver.tudelft.nl/uuid:e8110167-840f-43d0-8336-3c7d45e7f3e4","Magnetic Resonance Imaging motion correction in k-space: Detecting, estimating and correcting the bulk motion artifacts in k-space data","Rajwade, Rajesh (TU Delft Electrical Engineering, Mathematics and Computer Science)","Remis, R.F. (mentor); Tax, D.M.J. (graduation committee); Sbrizzi, Alessandro (graduation committee); Delft University of Technology (degree granting institution)","2019","Magnetic Resonance Imaging is a widely used technique to obtain images of the interior of the human body for diagnosis and treatment. MRI machines capture the raw signal in spatial frequency domain i.e. k-space and the image is obtained via Fourier transform. The Cartesian acquisition is one of the most commonly used acquisition patterns in MRI and is most susceptible to the patient's motion. Due to long scanning times, the possibility of the patient's movement is higher which introduces bulk motion artifacts reducing the quality of the image. Motion artifacts can affect the diagnosis and the necessity of re-scanning can cause significant financial costs as well as delays in diagnostics. Current methods for correcting motion artifacts work in image domain which need completely sampled k-space for reconstruction and hence are not useful for real-time artifacts correction. In this thesis, machine learning methods that can detect, estimate and correct motion artifacts in k-space were investigated making it possible to correct artifacts in real-time without the necessity of reconstruction. For each of these methods, we analyze the performance and discuss the merits and demerits.","MRI; Machine Learning; Image processing","en","master thesis","","","","","","","","","","","","","",""
"uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d","http://resolver.tudelft.nl/uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d","Segmenting and Detecting Carotid Plaque Components in MRI","van Hilten, Arno (TU Delft Mechanical, Maritime and Materials Engineering)","de Bruijne, Marleen (mentor); Sedghi Gamechi, Zahra (mentor); Niessen, Wiro (graduation committee); Kooij, Julian (graduation committee); Delft University of Technology (degree granting institution)","2018","Cardiovascular diseases and stroke are currently the leading causes of death worldwide. Atherosclerotic plaque is a mostly asymptotic vascular disease, but rupture of an atherosclerotic plaque in the carotid artery could lead to stroke. Automated segmentation of plaque components could help improve risk assessment by producing fast and reliable results while saving costs.
In this thesis two extensive comparisons have been made. First supervised classifiers are compared in the pixel-wise segmentation task of plaque components. In this comparison five conventional machine learning techniques and one deep learning architecture have been evaluated: linear and quadratic Bayes normal classifiers, linear logistic classifier, random forest and a U-net architecture. In the second comparison classifiers are evaluated in a detection task for their ability to learn with weakly labelled data. This is done within the multiple instance learning (MIL) framework. In addition to conventional multiple instance learning algorithms, a new MIL adaptation of the deep learning architecture, MIL U-net, is proposed and evaluated.
In the pixel-wise segmentation tasks the U-net architecture was the best overall classifier after the addition of 93 extra training patients to the original 20 training patients. A good inter-rater agreement was found for the haemorrhage class (ICC = 0.684) and the calcification class (ICC = 0.627). In the detection task the supervised methods, trained with one-sided noise, outperformed multiple instance classifiers such as MIL-Boost and the proposed MIL U-net. In this task both random forest and the linear logistic classifier obtained a fair Cohen's kappa (0.419 and 0.445 respectively) for detection of calcification per slice. The same classifiers obtained good correlation (Cohen's kappa 0.717 and 0.666 respectively) for haemorrhage detection per slice.","Machine Learning; Deep Learning; Multiple Instance Learning; Segmentation; Detection; Plaque Components; Carotid Artery; MRI","en","master thesis","","","","","","","","","","","","Mechanical Engineering","",""