Fully Convolutional Network for Liver Segmentation and Lesions Detection
| Autor: | Eyal Klang, Idit Diamant, Hayit Greenspan, Avi Ben-Cohen, M Amitai |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Deep learning Pattern recognition Computed tomography Classification scheme 02 engineering and technology Liver segmentation 030218 nuclear medicine & medical imaging Task (project management) 03 medical and health sciences 0302 clinical medicine Fully automatic 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Artificial intelligence business True positive rate |
| Zdroj: | Deep Learning and Data Labeling for Medical Applications ISBN: 9783319469751 LABELS/DLMIA@MICCAI |
| DOI: | 10.1007/978-3-319-46976-8_9 |
| Popis: | In this work we explore a fully convolutional network (FCN) for the task of liver segmentation and liver metastases detection in computed tomography (CT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions and 43 livers marked in one slice and 20 different patients with a full 3D liver segmentation. We ran 3-fold cross-validation and results indicate superiority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results. |
| Databáze: | OpenAIRE |
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