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Delft University of Technology
1.
Muga, Vamshi Krishna (author).
Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:3d4da29c-b048-4277-b281-345b83778511 
► 2D/3D registration is the process of aligning 3D volume data to a 2D image. Typically, during orthopedic trauma surgeries, 3D volume data is acquired prior…
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▼ 2D/3D registration is the process of aligning 3D volume data to a 2D image. Typically, during orthopedic trauma surgeries, 3D volume data is acquired prior to the surgery to visualize the extent of the injury and also plan the operation. During the surgery, 2D fluoroscopic images are acquired to visualize the position of surgical instruments and understand the progress of the operation. However, the depth perception from these 2D images becomes difficult. By employing 2D/3D registration to overlay pre-operative imaging modalities, doctors may be presented a better way to visualize the progress of the surgery. Although several approaches have been proposed in the literature to deal with 2D/3D registration, they are limited by the need for a good initial alignment. This requirement for accurate initial alignment is the main reason hindering the adoption of 2D/3D registration in clinical practice. In this thesis, we propose automated initialization strategies, the output of which can be used by any common 2D/3D registration algorithm for further fine tuning. A library based approach is to used to study the initialization problem. Four parameters of the six dimensional parameter space are sampled and their projections are stored prior to the surgery. We proposed frequency and feature based methods to retrieve the correct library match which is used for the initialization during the surgery. The feasibility of the approach is demonstrated by initializing an intensity based 2D/3D registration method with the automatically obtained estimation of the initialization transformation parameters. For fixed C-arm cases with available ground truth the initialization was successful for 87:41% for various anatomies such as the head, pelvis, spine, knees and feet. We further tested the approach in a completely un-calibrated scenario using a mobile C-arm. Visual evaluation of the results revealed a success rate of 88:4% for the initialization. Successful results were also obtained even in the presence of additional surgical instruments indicating the robustness of the proposed approach.
Advisors/Committee Members: Vilanova Bartroli, Anna (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: 2D/3D registration; Initialization; SURF features
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APA (6th Edition):
Muga, V. K. (. (2017). Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3d4da29c-b048-4277-b281-345b83778511
Chicago Manual of Style (16th Edition):
Muga, Vamshi Krishna (author). “Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries.” 2017. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:3d4da29c-b048-4277-b281-345b83778511.
MLA Handbook (7th Edition):
Muga, Vamshi Krishna (author). “Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries.” 2017. Web. 07 Mar 2021.
Vancouver:
Muga VK(. Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:3d4da29c-b048-4277-b281-345b83778511.
Council of Science Editors:
Muga VK(. Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:3d4da29c-b048-4277-b281-345b83778511
Delft University of Technology
2.
Zeng, Yun (author).
Spatio-Temporal Data Mining, Visual Analytics for Video Annotation.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:ed898b5a-ca47-45c0-acdb-f1c145494d97
► The explosion of video data in surveillance calls for large amount of annotated datasets that could be used for information retrieval, learning-based network training and…
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▼ The explosion of video data in surveillance calls for large amount of annotated datasets that could be used for information retrieval, learning-based network training and algorithms evaluation phase. A number of annotated video datasets have been shared to public, however, these open annotated datasets lack spatio-temporal information which can contribute to motion analysis researches. Moreover, manual annotation is tedious in lengthy frame sequences where the numbers are overwhelming. In this work we propose a visualization solution to facilitate video data retrieval, mining and annotation. It works as an integrated visual analytics system which supports collecting moving object samples and studying motion patterns in video, with the facilitation of video data visualization, using various spatial and/or temporal features, filtering parameters and similarity measuring models. The annotation output can be applied to multiple video analysis researches. In this paper, we present the system and propose a workflow for costeffective video annotation of spatio-temporal data as well as facilitating comprehension of video data.
Advisors/Committee Members: Vilanova Bartroli, Anna (mentor), Sepasian, Neda (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Visual Analytics; Video Annotation; Data Mining
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APA (6th Edition):
Zeng, Y. (. (2017). Spatio-Temporal Data Mining, Visual Analytics for Video Annotation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ed898b5a-ca47-45c0-acdb-f1c145494d97
Chicago Manual of Style (16th Edition):
Zeng, Yun (author). “Spatio-Temporal Data Mining, Visual Analytics for Video Annotation.” 2017. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:ed898b5a-ca47-45c0-acdb-f1c145494d97.
MLA Handbook (7th Edition):
Zeng, Yun (author). “Spatio-Temporal Data Mining, Visual Analytics for Video Annotation.” 2017. Web. 07 Mar 2021.
Vancouver:
Zeng Y(. Spatio-Temporal Data Mining, Visual Analytics for Video Annotation. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:ed898b5a-ca47-45c0-acdb-f1c145494d97.
Council of Science Editors:
Zeng Y(. Spatio-Temporal Data Mining, Visual Analytics for Video Annotation. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:ed898b5a-ca47-45c0-acdb-f1c145494d97
Delft University of Technology
3.
Schut, Dirk (author).
Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:97cded9d-50d2-4bb3-a130-835fdec48e5a
► Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated…
(more)
▼ Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated or frozen to destroy the tumor tissue. To guide the catheter, Ultrasound(US) imaging is used which shows the catheter position in real time. However, some tumors are not visible on US images. To make these tumors visible, image fusion can be used between the inter-operative US image and a pre-operative contrast enhanced CT(CECT) scan, on which the tumors are visible. Several methods exist for tracking the motions of the US transducer relative to the CECT scan, but they all require a manual initialization or external tracking hardware to align the coordinate systems of both scans. In this thesis we present a technique for finding an initialization using only the image data. To achieve this, deep learning is used to segment liver vessels and the boundary of the liver in 3D US images. To find the rigid transformation parameters, the SaDE evolutionary algorithm was used to optimize the alignment between the blood vessels and the liver boundary between both scans.
Computer Science and Electrical Engineering
Advisors/Committee Members: van Walsum, Theo (mentor), Vilanova Bartroli, Anna (mentor), Remis, Rob (mentor), Staring, Marius (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Image registration; Image fusion; Ablation; 3D Ultrasound; Registration initialization; Medical imaging
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APA (6th Edition):
Schut, D. (. (2018). Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:97cded9d-50d2-4bb3-a130-835fdec48e5a
Chicago Manual of Style (16th Edition):
Schut, Dirk (author). “Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations.” 2018. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:97cded9d-50d2-4bb3-a130-835fdec48e5a.
MLA Handbook (7th Edition):
Schut, Dirk (author). “Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations.” 2018. Web. 07 Mar 2021.
Vancouver:
Schut D(. Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:97cded9d-50d2-4bb3-a130-835fdec48e5a.
Council of Science Editors:
Schut D(. Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:97cded9d-50d2-4bb3-a130-835fdec48e5a
Delft University of Technology
4.
Grisel, Bastiaan (author).
The analysis of three-dimensional embeddings in Virtual Reality.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f
► Dimensionality reduction algorithms transform high-dimensional datasets with many attributes per observation into lower-dimensional representations (called embeddings) such that the structure of the dataset is maintained…
(more)
▼ Dimensionality reduction algorithms transform high-dimensional datasets with many attributes per observation into lower-dimensional representations (called embeddings) such that the structure of the dataset is maintained as well as possible. In this research, the use of Virtual Reality (VR) to analyse these embeddings has been evaluated and compared to the analysis on a desktop computer. The rationale for using VR is two-fold: three-dimensional embeddings generally better preserve the structure of a high-dimensional dataset than two-dimensional embeddings and the analysis of three-dimensional embeddings is difficult on desktop monitors. A user study (n=29) has been conducted in which participants performed the common analysis task of cluster identification. The task has been performed using a two-dimensional embedding on a desktop computer, a three-dimensional embedding on a desktop computer and a three-dimensional embedding in Virtual Reality. On average, participants that had at least used VR once before could better and more consistently identify clusters in the VR experiments compared to other methods. Participants found it easier to analyse a three-dimensional embedding in VR compared to analysing it on a desktop computer.
Computer Science | Data Science and Technology
Advisors/Committee Members: Eisemann, Elmar (mentor), Vilanova Bartroli, Anna (graduation committee), Brinkman, Willem-Paul (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: virtual; reality; embedding; visualisation; data; high-dimensional
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APA (6th Edition):
Grisel, B. (. (2018). The analysis of three-dimensional embeddings in Virtual Reality. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f
Chicago Manual of Style (16th Edition):
Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.
MLA Handbook (7th Edition):
Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Web. 07 Mar 2021.
Vancouver:
Grisel B(. The analysis of three-dimensional embeddings in Virtual Reality. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.
Council of Science Editors:
Grisel B(. The analysis of three-dimensional embeddings in Virtual Reality. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f
Delft University of Technology
5.
Jiang, Huinan (author).
Player model analysis for adaptive content delivery in an educational game.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:67e93fc8-5651-4694-a8d8-3cda74e71fe2
► Gamification and game-based learning have received wide attention in the past few decades. By blending game design and mechanics into traditional learning environment, they enhance…
(more)
▼ Gamification and game-based learning have received wide attention in the past few decades. By blending game design and mechanics into traditional learning environment, they enhance students’ participation, motivation and engagement. Squla is such a gamified learning platform where we can find game components like coins, virtual shop, competitive activities and collectables. Besides the above gamified elements, Squla has also transformed standard questions into fun games such as shooting catapults and clicking the popping bubbles. These games are designed to further engage the students and improve their learning. In this project, we analysed player game type preference based on their game log data, and measure the impact of customised game type delivery. We targeted education group 4 and 5 students users, and focus on catapult games and bubble popper games as they are the most played. A set of features that could reflect students’ preference and emotion states are selected and analysed, including correct ratio, playtime, quitting possibility, etc. Using data clustering, we group students who have similar behaviour and predict their preferred game types. We identified three group of students, one shows high completion rate on all forms of questions, another shows rather low overall completion rate, and the last group has rather high completion rate on bubble popper games and lower completion rate on the catapult shooting games. Based on such findings, we conducted experiment on them to look into different gaming contents’ impact on their learning and engagement. A final experiment consists of a short math quiz and a follow-up questionnaire. The two-week online experiment receives 91 valid responses. Post-play questionnaire, as well as the game log suggest different contents could affect students’ engagement. In particular, preferred contents can elevate a sense of happiness and enhance perceived learning.
Computer Science
Advisors/Committee Members: Bidarra, Rafa (mentor), Vilanova Bartroli, Anna (graduation committee), Bekebrede, Geertje (graduation committee), Delft University of Technology (degree granting institution).
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APA (6th Edition):
Jiang, H. (. (2019). Player model analysis for adaptive content delivery in an educational game. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:67e93fc8-5651-4694-a8d8-3cda74e71fe2
Chicago Manual of Style (16th Edition):
Jiang, Huinan (author). “Player model analysis for adaptive content delivery in an educational game.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:67e93fc8-5651-4694-a8d8-3cda74e71fe2.
MLA Handbook (7th Edition):
Jiang, Huinan (author). “Player model analysis for adaptive content delivery in an educational game.” 2019. Web. 07 Mar 2021.
Vancouver:
Jiang H(. Player model analysis for adaptive content delivery in an educational game. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:67e93fc8-5651-4694-a8d8-3cda74e71fe2.
Council of Science Editors:
Jiang H(. Player model analysis for adaptive content delivery in an educational game. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:67e93fc8-5651-4694-a8d8-3cda74e71fe2
Delft University of Technology
6.
Alashrafov, Rustam (author).
VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:57ef7468-c53b-44c9-87b1-a8f698305831
► Touchless interaction has recently gained considerable attention by researchers as well as industry. Different domains are interested in implementing this technology in their solutions. Medical…
(more)
▼ Touchless interaction has recently gained considerable attention by researchers as well as industry. Different domains are interested in implementing this technology in their solutions. Medical visualization has a special interest in this technology due to the sterile conditions in operating rooms. Exploration and detailed inspection of the scanned objects are among the most common interactions performed by professionals. These operations become more challenging when combined with touchless input. Context-aware methods exist, which facilitate navigation, but these methods are made for meshes and not for volume renderings. Hence the research question: Can these methods be extended to volume renderings and how well will they perform with touchless interaction metaphors? Metaphor and underlying VolCam algorithm are presented in this work. The metaphor allows users to perform exploration and inspection tasks on medical volume data using touchless input device - LeapMotion. The VolCam - an extension of the ShellCam algorithm, automatically maps the user input to distinct camera movements based on the current scene view by sampling the visible part of the volume. Interactive frame rates are achieved by performing computations on GPU. No pre-processing or specialized data structures are required which makes the technique directly applicable to wide-range of volume datasets.
3JECTOR
Advisors/Committee Members: Eisemann, Elmar (mentor), Vilanova Bartroli, Anna (mentor), Katramados, Ioannis (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: interfaces; touchless; intuitive; interaction; volume rendering; medical; NUI
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APA (6th Edition):
Alashrafov, R. (. (2017). VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:57ef7468-c53b-44c9-87b1-a8f698305831
Chicago Manual of Style (16th Edition):
Alashrafov, Rustam (author). “VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data.” 2017. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:57ef7468-c53b-44c9-87b1-a8f698305831.
MLA Handbook (7th Edition):
Alashrafov, Rustam (author). “VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data.” 2017. Web. 07 Mar 2021.
Vancouver:
Alashrafov R(. VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:57ef7468-c53b-44c9-87b1-a8f698305831.
Council of Science Editors:
Alashrafov R(. VolCam: Context-Aware Intuitive Touchless Interaction For Medical Volume Data. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:57ef7468-c53b-44c9-87b1-a8f698305831
Delft University of Technology
7.
Yin, Yunchao (author).
Automatic Labeling of X-Ray Images Based on Deep Learning.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:8549a58e-a542-4504-aa55-f642004373b9
► Coronary artery disease is the most common type of heart disease, which influences 110 million people's health and causes 8.9 million deaths in 2015. Physicians…
(more)
▼ Coronary artery disease is the most common type of heart disease, which influences 110 million people's health and causes 8.9 million deaths in 2015. Physicians can visualize the lesion in coronary arteries by cardiac angiography (X-ray image) during diagnosis and treatment of coronary artery disease. The pathological findings in cardiac angiography are reported per segment or per artery of the coronary artery tree, therefore, it requires to annotate the name of each segment or artery in the coronary artery tree. This thesis proposes a data-driven method as a first attempt at annotating cardiac angiography based on deep learning. The method aims at automatically regressing segment points between different segments on the coronary artery tree as the annotation of the cardiac angiography. The proposed data-driven cardiac angiography annotation methods can learn and generalize from manually annotated cardiac angiography examples, but its performance is limited by the number and quality of examples for learning.
Computer Science
Advisors/Committee Members: Vilanova Bartroli, Anna (mentor), Van Pelt, Roy (mentor), Oliván Bescós, Javier (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Cardiac angiography annotation; Deep Learning
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APA (6th Edition):
Yin, Y. (. (2018). Automatic Labeling of X-Ray Images Based on Deep Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8549a58e-a542-4504-aa55-f642004373b9
Chicago Manual of Style (16th Edition):
Yin, Yunchao (author). “Automatic Labeling of X-Ray Images Based on Deep Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:8549a58e-a542-4504-aa55-f642004373b9.
MLA Handbook (7th Edition):
Yin, Yunchao (author). “Automatic Labeling of X-Ray Images Based on Deep Learning.” 2018. Web. 07 Mar 2021.
Vancouver:
Yin Y(. Automatic Labeling of X-Ray Images Based on Deep Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:8549a58e-a542-4504-aa55-f642004373b9.
Council of Science Editors:
Yin Y(. Automatic Labeling of X-Ray Images Based on Deep Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:8549a58e-a542-4504-aa55-f642004373b9
Delft University of Technology
8.
van Wijnen, Kimberlin (author).
Detecting Perivascular Spaces: a Geodesic Deep Learning Approach.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:0696f548-97b8-4b32-b21c-cb5b95ed02eb
► Perivascular spaces (PVS) visible on MRI are currently emerging as an important potential neuroimaging marker for several pathologies in the brain like Alzheimer’s disease and…
(more)
▼ Perivascular spaces (PVS) visible on MRI are currently emerging as an important potential neuroimaging marker for several pathologies in the brain like Alzheimer’s disease and cerebral small vessel disease. PVS are fluid-filled spaces surrounding vessels as they enter the brain. Although PVS are normally not noticeable on MRI scans acquired at clinical field strengths, when these spaces increase in size they become increasingly visible and quantifiable. To study these spaces it is important to have a robust method for quantifying PVS. Manual quantification of PVS is challenging, time-consuming and subject to observer bias due to the difficulty of distinguishing PVS from mimics and the large number of PVS that can occur in MRI scans. Many promising (semi-)automated methods have been proposed recently to decrease annotation time and intra- and inter-observer variability while providing more information about EPVS. However there are still various limitations in the current methods that need to be overcome. An important limitation is that most of the methods are based on elaborate preprocessing steps, feature extraction and heuristic fine-tuning of parameters, making the use of these methods on new datasets cumbersome. Furthermore the majority of the currently proposed methods have been evaluated on small sets of barely 30 images, as most of these methods aim to segment PVS and require voxel-wise annotations for evaluation. In this thesis we propose a method for automated detection of perivascular spaces that combines a convolutional neural network and geodesic distance transform (GDT). We propose to use dot annotations instead of voxel-wise segmentations as this is less time-consuming than fully segmenting PVS while still providing the location of PVS. This enables us to use a considerably larger dataset with ground truth locations than is used in all previously proposed (semi-)automatic methods that provide the location of PVS. We investigated two approaches of using geodesic distance transform to optimize the CNN to detect PVS. The first approach focuses on optimizing the CNN for voxel-wise regression of the geodesic distance map (GDM) computed from the dots and the intensity image. The second approach aims to predict segmentations of the PVS using a CNN that is trained on approximated segmentations obtained by thresholding GDMs. We use 1202 proton density-weighted (PDw) MRI scans to develop our methods and 1000 other scans are used to evaluate the performance of the methods. We show that our methods match human intra-rater performance on detecting PVS without the need for any user interaction. Additionally we show that GDMs are extremely useful for capturing complex morphologies when computed from dot annotations. Our experiments indicate that GDMs can be used to provide valuable additional information to CNNs during training.
Advisors/Committee Members: Dubost, Florian (mentor), de Bruijne, Marleen (mentor), Niessen, Wiro (graduation committee), Vos, Frans (graduation committee), Vilanova Bartroli, Anna (graduation committee), Staring, Marius (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Deep learning; perivascular spaces; detection; geodesic distance transform; dot annotations; weighted loss
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APA (6th Edition):
van Wijnen, K. (. (2018). Detecting Perivascular Spaces: a Geodesic Deep Learning Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0696f548-97b8-4b32-b21c-cb5b95ed02eb
Chicago Manual of Style (16th Edition):
van Wijnen, Kimberlin (author). “Detecting Perivascular Spaces: a Geodesic Deep Learning Approach.” 2018. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:0696f548-97b8-4b32-b21c-cb5b95ed02eb.
MLA Handbook (7th Edition):
van Wijnen, Kimberlin (author). “Detecting Perivascular Spaces: a Geodesic Deep Learning Approach.” 2018. Web. 07 Mar 2021.
Vancouver:
van Wijnen K(. Detecting Perivascular Spaces: a Geodesic Deep Learning Approach. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:0696f548-97b8-4b32-b21c-cb5b95ed02eb.
Council of Science Editors:
van Wijnen K(. Detecting Perivascular Spaces: a Geodesic Deep Learning Approach. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0696f548-97b8-4b32-b21c-cb5b95ed02eb
Delft University of Technology
9.
Reichert, Gijs (author).
A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:d29ec61e-fcef-4510-8d6c-48af54e94bf8
► Nowadays video plays an important role in the coaching of athletes across many different sports. To make more use of the advantages videos can provide…
(more)
▼ Nowadays video plays an important role in the coaching of athletes across many different sports. To make more use of the advantages videos can provide for coaching, the Dutch Sailing team is shifting from manually recording short videos towards continuously recording training sessions. This new recording approach provides opportunities and creates challenges at the same time. In this thesis we present a pipeline to address the problems with the stability of the recording and the first steps towards a Visual Analysis Framework, which leverages the available video data. New information is extracted from the video recordings by detecting and tracking the boat and sailors. Moreover, we semi-automatically highlight interesting intervals in time of a recorded training session. These are the first steps towards an extensive Visual Analysis Framework which has the potential to make the analysis of the videos easier and provide the coaches with tools to improve the analysis of the performance of the sailors.
Advisors/Committee Members: Vilanova Bartroli, Anna (mentor), Marroquim, Ricardo (graduation committee), van Gemert, Jan (graduation committee), van der Heijden, Gert (graduation committee), Broekens, Douwe (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Sailing; Video; Visual Analytics; Dinghy
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APA (6th Edition):
Reichert, G. (. (2020). A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d29ec61e-fcef-4510-8d6c-48af54e94bf8
Chicago Manual of Style (16th Edition):
Reichert, Gijs (author). “A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions.” 2020. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:d29ec61e-fcef-4510-8d6c-48af54e94bf8.
MLA Handbook (7th Edition):
Reichert, Gijs (author). “A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions.” 2020. Web. 07 Mar 2021.
Vancouver:
Reichert G(. A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:d29ec61e-fcef-4510-8d6c-48af54e94bf8.
Council of Science Editors:
Reichert G(. A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:d29ec61e-fcef-4510-8d6c-48af54e94bf8
Delft University of Technology
10.
Gasparini, Lorenzo (author).
Visualisation of Code Changes for Code Review.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:4253425a-8cc3-4469-85e3-0123dbb60713
► Code reviews are a widely adopted practice in software engineering that is proven to increase the quality of the code. Despite its evolution in the…
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▼ Code reviews are a widely adopted practice in software engineering that is proven to increase the quality of the code. Despite its evolution in the last decade, it still presents a number of challenges, such as understanding the changeset in review. In this thesis we research the usage of Software Visualisation paradigms to aid reviewers in the change-understanding process with a tool-based approach. Based on a survey of the code change visualisation and code navigation research areas, we devise a set of candidate prototypes of a cognitive support review tool, which we iteratively refine involving developers in the process. Through an online survey, we select one of them and build CHANGEVIZ, the implementation of our novel code review environment. The effectiveness of our approach is validated with a preliminary experiment in which developers perform change-review tasks in our review environment. The results suggest that the features incorporated by our tool are valuable for reviewers.
Computer Science
Advisors/Committee Members: Bacchelli, Alberto (mentor), Finavaro Aniche, Mauricio (graduation committee), Vilanova Bartroli, Anna (graduation committee), Baum, Tobias (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: software engineering; code reviews; software visualisation
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APA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gasparini, L. (. (2019). Visualisation of Code Changes for Code Review. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4253425a-8cc3-4469-85e3-0123dbb60713
Chicago Manual of Style (16th Edition):
Gasparini, Lorenzo (author). “Visualisation of Code Changes for Code Review.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:4253425a-8cc3-4469-85e3-0123dbb60713.
MLA Handbook (7th Edition):
Gasparini, Lorenzo (author). “Visualisation of Code Changes for Code Review.” 2019. Web. 07 Mar 2021.
Vancouver:
Gasparini L(. Visualisation of Code Changes for Code Review. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:4253425a-8cc3-4469-85e3-0123dbb60713.
Council of Science Editors:
Gasparini L(. Visualisation of Code Changes for Code Review. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:4253425a-8cc3-4469-85e3-0123dbb60713
Delft University of Technology
11.
Gabriel, Luka (author).
MRI-based virtual CT generation from unpaired data.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018
► Both MRI and CT imaging are commonly used and combined in medical imaging because of their complementary information about soft tissue and bone respectively. However,…
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▼ Both MRI and CT imaging are commonly used and combined in medical imaging because of their complementary information about soft tissue and bone respectively. However, CT imaging relies on harmful ionizing radiation. Thus medical imaging scientists are working on transferring harmless MRI scans to CT-like images where using deep learning methods are the current state of the art. Most deep learning methods however require large amounts of paired data to train the transfer systems, which is why we try to perform the transfer using an unpaired method, CycleGANs. Using ex-vivo dog hip MRI and CT scan pairs we trained a CycleGAN and a U-Net using both registered and poorly registered data. We also further investigated the effect of discriminator’s receptive field size in a CycleGAN as well as introduced a novel NMI loss component to its generators. Performance of said systems was evaluated by calculating MAE and Dice scores. We show that CycleGAN's performance is sensitive to its discriminator's receptive field size and can even generate unwanted structrures in the CT-like images. Also, we show that adding NMI loss component with the right weight can improve the system’s performance. The results of comparing the pairwise and unpaired method show that a CycleGAN will only outperform the U-Net when transferring poorly registered data. This suggest that, whenever paired registered data is available, it is better to use a pairwise transfer approach. However, the results of the experiment where the NMI loss component was added suggest that transfer from MRI scans to CT-like images can become more accurate and encourage further research. Finally, this means in certain cases CT scans could be avoided, providing a more harmless medical imaging future.
EIT Digital
Advisors/Committee Members: Loog, Marco (mentor), Vilanova Bartroli, Anna (graduation committee), van Gemert, Jan (graduation committee), Hildebrandt, Klaus (graduation committee), van Stralen, Marijn (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Medical Imaging; MRI; CT; Deep Learning; CycleGAN
Record Details
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Record Details
Similar Records
Cite
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gabriel, L. (. (2018). MRI-based virtual CT generation from unpaired data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018
Chicago Manual of Style (16th Edition):
Gabriel, Luka (author). “MRI-based virtual CT generation from unpaired data.” 2018. Masters Thesis, Delft University of Technology. Accessed March 07, 2021.
http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018.
MLA Handbook (7th Edition):
Gabriel, Luka (author). “MRI-based virtual CT generation from unpaired data.” 2018. Web. 07 Mar 2021.
Vancouver:
Gabriel L(. MRI-based virtual CT generation from unpaired data. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 07].
Available from: http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018.
Council of Science Editors:
Gabriel L(. MRI-based virtual CT generation from unpaired data. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018
. 
Open Access Theses and Dissertations