Deep learning approach to cervical segmentation from routine CT images
Recently, techniques for 3D medical image segmentation have become increas ingly sophisticated. Different types of Unet-based segmentation networks per form very well in tasks where the liver, blood vessels or brain are the seg mentation targets. Yet, compared to these holistic segmentation targe...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/168606 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Recently, techniques for 3D medical image segmentation have become increas
ingly sophisticated. Different types of Unet-based segmentation networks per
form very well in tasks where the liver, blood vessels or brain are the seg
mentation targets. Yet, compared to these holistic segmentation targets, another
equally important class of human tissues, the bone, has a more pronounced se
quential character. Traditional 3D methods are unable to learn the exact position
of bone joints due to the limitation of local similarity and numbers of param
eters. To address the sequential and location-dependent nature of bone segmen
tation information, the dissertation proposes a pre-training model for location
information, and combines 2.5D image input and labeled multi-channel coding
to semantically segment 3D cervical spine CT images. This method can accu
rately extract the information of bone joint location of slices and incorporate it
into image features in the form of channels, which can improve the accuracy
of multiple bone joint segmentation and classification. The experimental results
show that this approach achieves better segmentation results in the test set. |
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