Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution
| Autor: | Olga V. Senyukova, Gregory Borodin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Ventricle 0202 electrical engineering electronic engineering information engineering Benchmark (computing) medicine 020201 artificial intelligence & image processing Segmentation Artificial intelligence Mr images business Endocardium |
| Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2018 ISBN: 9783030014209 ICANN (2) |
| DOI: | 10.1007/978-3-030-01421-6_18 |
| Popis: | Segmentation of anatomical structures in cardiac MR images is an important problem because it is necessary for evaluation of morphology of these structures for diagnostic purposes. Automatic segmentation algorithm with near-human accuracy would be extremely helpful for a medical specialist. In this paper we consider such structures as endocardium and epicardium of right ventricle. We compare the performance of the best existing neural networks such as U-Net and GridNet, and propose our own modification of U-Net which implies replacement of every second convolution layer with dilated (atrous) convolution layer. Evaluation on benchmark dataset RVSC demonstrated that the proposed algorithm allows to improve the segmentation accuracy up to 6% both for endocardium and epicardium compared to original U-Net. The algorithm also overperforms GridNet for both segmentation problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|