Using deep learning to remove computed tomography artifacts due to hip replacement
(2020) In Master's Theses in Mathematical Sciences FMAM05 20201Mathematics (Faculty of Engineering)
- Abstract
- Computed Tomography (CT) is the most common diagnostic method for cancer and prostate cancer is the most common cancer among men in Sweden. Some of the patients that get scanned have hip replacements that cause artifacts in CT scans that make the CT images unreadable for physicians. In this Master’s Thesis an autoencoder model was implemented to reduce artifacts due to hip replacements in CT images. The model was trained, validated and tested on CT images provided by EXINI Diagnostics AB, Lund, Sweden.
The autoencoder was implemented using the deep learning framework Keras in Python. Autoencoders have been used to reduce different types of noise in other experiments and shown great results. The produced results show that the model is a... (More) - Computed Tomography (CT) is the most common diagnostic method for cancer and prostate cancer is the most common cancer among men in Sweden. Some of the patients that get scanned have hip replacements that cause artifacts in CT scans that make the CT images unreadable for physicians. In this Master’s Thesis an autoencoder model was implemented to reduce artifacts due to hip replacements in CT images. The model was trained, validated and tested on CT images provided by EXINI Diagnostics AB, Lund, Sweden.
The autoencoder was implemented using the deep learning framework Keras in Python. Autoencoders have been used to reduce different types of noise in other experiments and shown great results. The produced results show that the model is a good start for further work to completely reduce artifacts due to hip replacement. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9008353
- author
- Zahirovic, Adelina LU
- supervisor
-
- Karl Åström LU
- organization
- alternative title
- Djupa faltningsnätverk för borttagning av protesorsakade artefakter i datortomografiska bilder
- course
- FMAM05 20201
- year
- 2020
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Image analysis
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3406-2020
- ISSN
- 1404-6342
- other publication id
- 2020:E23
- language
- English
- id
- 9008353
- date added to LUP
- 2020-05-25 17:00:32
- date last changed
- 2022-04-01 03:39:38
@misc{9008353, abstract = {{Computed Tomography (CT) is the most common diagnostic method for cancer and prostate cancer is the most common cancer among men in Sweden. Some of the patients that get scanned have hip replacements that cause artifacts in CT scans that make the CT images unreadable for physicians. In this Master’s Thesis an autoencoder model was implemented to reduce artifacts due to hip replacements in CT images. The model was trained, validated and tested on CT images provided by EXINI Diagnostics AB, Lund, Sweden. The autoencoder was implemented using the deep learning framework Keras in Python. Autoencoders have been used to reduce different types of noise in other experiments and shown great results. The produced results show that the model is a good start for further work to completely reduce artifacts due to hip replacement.}}, author = {{Zahirovic, Adelina}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Using deep learning to remove computed tomography artifacts due to hip replacement}}, year = {{2020}}, }