Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation
| Autor: | Götz Thomalla, Benedikt M. Frey, Annika Hänsch, Iris Lettow, Marvin Petersen, Horst K. Hahn, Bastian Cheng, Farhad Yazdan Shenas, Jan Klein, Carola Mayer |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging ISBN: 9783030326944 OR/MLCN@MICCAI |
| DOI: | 10.1007/978-3-030-32695-1_10 |
| Popis: | White Matter Hyperintensities (WMH) are imaging biomarkers which indicate cerebral microangiopathy, a risk factor for stroke and vascular dementia. When training Deep Neural Networks (DNN) to segment WMH, data pooling may be used to increase the training dataset size. However, it is not yet fully understood how pooling of heterogeneous data influences the segmentation performance. In this contribution, we investigate the impact of sampling ratios between different datasets with varying data quality and lesion volumes. We observe systematic changes in DNN performance and segmented lesion volume depending on the sampling ratio. If properly chosen, a single DNN can accurately segment and quantify both large and small lesions on different quality test data without loss of performance compared with a specialized DNN. |
| Databáze: | OpenAIRE |
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