Medical image segmentation using shape prior information and deep neural  networks

Petryshak, Bohdan

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Students Research & Project Works | Роботи студентів
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Факультет прикладних наук
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Бакалаврська програма "Комп'ютерні науки"
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2019
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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