Show simple item record

dc.contributor.advisorZilles, Sandra
dc.contributor.authorDhar Tupor, Shayantonee
dc.date.accessioned2022-08-05T17:52:06Z
dc.date.available2022-08-05T17:52:06Z
dc.date.issued2021-08
dc.identifier.urihttp://hdl.handle.net/10294/15035
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. xiii, 90 p.en_US
dc.description.abstractDuring brachytherapy sessions, medical specialists record ultrasound images of prostate cancer patients and segment these images manually. In the process of analyzing patient records, it is a fundamental task to localize the catheters (needles) in the recorded ultrasound images. Due to the signi cant amount of noise in ultrasound images, localizing multiple catheter positions in ultrasound images is more challenging than similar image segmentation tasks for MRI and CT images. The manual segmentation process is very time-consuming and relies on experienced clinicians. Hence, a tool for the automatic localization of catheters in ultrasound images is highly desirable. In the medical eld, deep learning has gained popularity due to its ability to produce highly accurate detection tools. In order to automate the segmentation of ultrasound images in general and the detection of catheters in particular, we utilize a deep neural network-based architecture named U-net. A 5-fold cross-validation method is applied to evaluate the performance of the U-net based model on the limited dataset. After training a deep neural network, we further improve the resulting detection model by incorporating domain information provided by a medical expert. i The second step of our approach is to generate 3D detections from a combination of the 2D detections, using sequences of 2D images for each patient. Through these 3D detections, we obtain an improvement in accuracy compared to the 2D model.en_US
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.titleSegmentation of Ultrasound Images Using Deep Neural Networksen_US
dc.typeThesisen_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
thesis.degree.nameMaster of Science (MSc)en_US
thesis.degree.levelMaster'sen
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Reginaen
thesis.degree.departmentDepartment of Computer Scienceen_US
dc.contributor.committeememberSadaoui, Samira
dc.contributor.externalexaminerVolodin, Andrei
dc.identifier.tcnumberTC-SRU-15035
dc.identifier.thesisurlhttps://ourspace.uregina.ca/bitstream/handle/10294/15035/DharTupor_Shayantonee_MSC_CS_Spring2021.pdf


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record