Realtime Magnetic Resonance Imaging (MRI) is a method used for human
anatomical study. MRIs give exceptionally detailed information about soft-tissue
structures, such as tongues, that other current imaging techniques cannot achieve.
However, the process requires special equipment and is expensive. Hence, it is not quite
suitable for all patients.
Speech diagrams show the side view positions of organs like the tongue, throat,
and lip of a speaking or singing person. The process of making a speech diagram is like
the semantic segmentation of an MRI, which focuses on the selected edge structure.
Speech diagrams are easy to understand with a clear speech diagram of the tongue and
inside mouth structure. However, it often requires manual annotation on the MRI
machine by an expert in the field.
By using machine learning methods, we achieved transferring images between
MRI and speech diagrams in two directions. We first matched videos of speech diagram
and tongue MRIs. Then we used various image processing methods and data
augmentation methods to make the paired images easy to train. We built our network
model inspired by different cross-domain image transfer methods and applied
reference-based super-resolution methods—to generate high-resolution images. Thus,
we can do the transferring work through our network instead of manually. Also,
generated speech diagram can work as an intermediary part to be transferred to other
medical images like computerized tomography (CT), since it is simpler in structure
compared to an MRI.
We conducted experiments using both the data from our database and other MRI
video sources. We use multiple methods to do the evaluation and comparisons with
several related methods show the superiority of our approach.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/41533 |
| Date | 03 December 2020 |
| Creators | Wang, Kang |
| Contributors | Lee, Wonsook |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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