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You searched for +publisher:"Delft University of Technology" +contributor:("Vos, Frans"). Showing records 1 – 3 of 3 total matches.

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Delft University of Technology

1. van Hoek, Bob (author). MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine.

Degree: 2019, Delft University of Technology

Background: For both hospitals and patients it would be beneficial if the scan time of MR images could be reduced. At the moment, Compressed Sensing (CS) is introduced to reduce the scan time, however, new methods are developed such as a deep learning method, called the Recurrent Inference Machine (RIM). In this study the effect of reconstructing undersampled MRI images with lesions, using the RIM and CS, was evaluated. In data of a healthy control, lesions were simulated. Evaluation is done by checking if the lesion has the correct intensity and shape after reconstruction of undersampled data. Methods: In raw data of a healthy control lesions were simulated. To test the RIM and CS, the images with lesions where first undersampled 4x, 6x, 8x and 10x. After undersampling, the images were reconstructed with both RIM and CS. First, the peak intensity difference was measured between the reference image (with simulated lesions) and reconstructed images for both RIM and CS. Second, one lesion was undersampled ten times with different undersampling masks creating different noise, for 3 different acceleration factors (4x, 6x, 8x). These lesions were reconstructed with both RIM and CS. The maximum intensity difference between reference and reconstructed image was measured and averaged over the ten different undersampled images. Results: In total seven different lesions were simulated in a healthy control with different intensities varying between 10% and 100% of the GM-lesion intensity in a FLAIR weighted scan. The intensities of all lesions were more accurately reconstructed with the RIM compared to CS at higher acceleration factors: the average intensity per lesions after 10 times reconstruction with RIM was more equal to the correct intensity compared to the reconstruction with CS. Conclusion: The RIM shows robust and accurate results on data with simulated lesions. Moreover, the RIM outperformed CS on data that was more undersampled. Therefore, the RIM may be used for reconstruction of MRI data that is acquired with shorter acquisition time. And since the reconstruction time is better, it could replace CS in the future. However, before the RIM could be used, further evaluations on actual patient data are needed. Advisors/Committee Members: Vos, Frans (mentor), Caan, Matthan WA (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: MRI; Image Reconstruction; Deep Learning; Lesion Simulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Hoek, B. (. (2019). MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3aa8c68e-3135-4b86-b256-8c853a409960

Chicago Manual of Style (16th Edition):

van Hoek, Bob (author). “MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine.” 2019. Masters Thesis, Delft University of Technology. Accessed March 05, 2021. http://resolver.tudelft.nl/uuid:3aa8c68e-3135-4b86-b256-8c853a409960.

MLA Handbook (7th Edition):

van Hoek, Bob (author). “MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine.” 2019. Web. 05 Mar 2021.

Vancouver:

van Hoek B(. MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 05]. Available from: http://resolver.tudelft.nl/uuid:3aa8c68e-3135-4b86-b256-8c853a409960.

Council of Science Editors:

van Hoek B(. MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:3aa8c68e-3135-4b86-b256-8c853a409960


Delft University of Technology

2. Lelekas, Ioannis (author). Top-Down Networks: A coarse-to-fine reimagination of CNNs.

Degree: 2020, Delft University of Technology

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In the current paper we propose the extraction of top-down networks, by reversing the feature extraction part of the baseline, bottom-up architecture. This coarse-to-fine pathway, by blurring out higher frequency information and restoring it only at later stages, offers a line of defence against attacks introducing high frequency noise. High resolution of the final convolutional layer's feature map can contribute to the transparency of the network's decision making process, as well as favor more object-driven decisions over context driven ones and thus provide better localized class activation maps. The paper offers empirical evidence for the applicability of the method to various existing architectures, but also on multiple visual recognition tasks. Advisors/Committee Members: van Gemert, Jan (mentor), Reinders, Marcel (graduation committee), Vos, Frans (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Top-Down; Fine-to-Coarse; Coarse-to-Fine; Adversarial attacks; Adversarial robustness; Gradcam; Object localization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lelekas, I. (. (2020). Top-Down Networks: A coarse-to-fine reimagination of CNNs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Chicago Manual of Style (16th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Masters Thesis, Delft University of Technology. Accessed March 05, 2021. http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

MLA Handbook (7th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Web. 05 Mar 2021.

Vancouver:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 05]. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

Council of Science Editors:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345


Delft University of Technology

3. Meij, Senna (author). The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings.

Degree: 2019, Delft University of Technology

The operating room is one of the most complex and expensive environments in the hospital. Research has been focusing on improving the efficient use of the OR time, for instance by using intraoperative data to update the planning of the OR during the day. This thesis used a deep learning network to automatically recognize surgical tools and pre-defined surgical phases present in the recordings, to ultimately track the progress of the procedure. The aim of this thesis is to assess the performance and applicability of this deep learning method for the use of image recognition in a medical environment. To ultimately predict the remaining surgery duration and improve the efficiency of the OR planning. Two datasets of laparoscopic recordings were used, one containing laparoscopic cholecystectomies and one containing total laparoscopic hysterectomies. The surgical tools and the pre-defined phases of the procedure were annotated in every recording, after which the deep learning network was trained with this data. The performance of the network was tested in multiple experiments. The results showed that the performance of the deep learning network was promising and in line with published literature, but that the results varied between recordings. An experiment using three different sized datasets showed that a larger dataset corresponded with the best results and results that varied the least between recordings. Testing the generalizability of the network showed that a network trained on one type of surgery can also be used to recognize similar tools in a different type of surgery. Important is that the tools have the same design. It was found that the most important resources for a project like this are a dedicated hardware with image recognition software and time. This thesis showed the applicability of a deep learning network to automatically recognize the progress of a surgery and provided insight into the steps that need to be taken to use it on a larger scale.

BioMedical Engineering

Advisors/Committee Members: van den Dobbelsteen, John (mentor), Vos, Frans (graduation committee), Vijfvinkel, Teddy (graduation committee), Gu├ędon, A.C.P. (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Deep learning; Image recognition; surgery progress

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Meij, S. (. (2019). The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:28f96ac7-f5ae-43af-9ba0-14832af5c103

Chicago Manual of Style (16th Edition):

Meij, Senna (author). “The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings.” 2019. Masters Thesis, Delft University of Technology. Accessed March 05, 2021. http://resolver.tudelft.nl/uuid:28f96ac7-f5ae-43af-9ba0-14832af5c103.

MLA Handbook (7th Edition):

Meij, Senna (author). “The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings.” 2019. Web. 05 Mar 2021.

Vancouver:

Meij S(. The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 05]. Available from: http://resolver.tudelft.nl/uuid:28f96ac7-f5ae-43af-9ba0-14832af5c103.

Council of Science Editors:

Meij S(. The applicability of deep learning to detect the progress of laparoscopic surgery using video recordings. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:28f96ac7-f5ae-43af-9ba0-14832af5c103

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