dc.contributor.author |
Petryshak, Bohdan
|
|
dc.date.accessioned |
2024-02-14T16:59:26Z |
|
dc.date.available |
2024-02-14T16:59:26Z |
|
dc.date.issued |
2019 |
|
dc.identifier.citation |
Petryshak, Bohdan. Medical image segmentation using shape prior information and deep neural networks / Petryshak, Bohdan; Supervisor: Jan Kybic; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2019. – 47 p. |
uk |
dc.identifier.uri |
https://er.ucu.edu.ua/handle/1/4485 |
|
dc.language.iso |
en |
uk |
dc.title |
Medical image segmentation using shape prior information and deep neural networks |
uk |
dc.type |
Preprint |
uk |
dc.status |
Публікується вперше |
uk |
dc.description.abstracten |
Semantic image segmentation is the task of classifying each pixel of an image into
a corresponding category of what is being represented. It is an essential step to-
wards automating image analysis process. However, the low-quality signal, high
level of noise, variety of objects appearance, little amount of labeled data are the
critical obstacles which stand on the way of achieving the perfect segmentation re-
sults. Incorporating the shape prior knowledge has proven significant improvement
of the segmentation results. In this work, we extend the existing method of incorpo-
rating shape priors within the FCN segmentation framework to a multiclass seman-
tic segmentation. We demonstrate the superiority of our extension in five different
datasets and show that it capable of making the segmentation results more accurate
and plausible in multiclass problems. . . . |
uk |