Activities per year
Abstract
In this contribution, we propose an automatic ground truth generation approach that utilizes Positron Emission Tomography (PET) acquisitions to train neural networks for automatic urinary bladder segmentation in Computed Tomography (CT) images. We evaluated different deep learning architectures to segment the urinary bladder. However, deep neural networks require a large amount of training data, which is currently the main bottleneck in the medical field, because ground truth labels have to be created by medical experts on a time-consuming slice-by-slice basis. To overcome this problem, we generate the training data set from the PET data of combined PET/CT acquisitions. This can be achieved by applying simple thresholding to the PET data, where the radiotracer accumulates very distinct in the urinary bladder. However, the ultimate goal is to entirely skip PET imaging and its additional radiation exposure in the future, and only use CT images for segmentation.
Original language | English |
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DOIs | |
Publication status | Published - 2019 |
Event | 2018 IEEE Biomedical Engineering International Conference - Chiang Mai, Thailand Duration: 21 Nov 2018 → … Conference number: 11 |
Conference
Conference | 2018 IEEE Biomedical Engineering International Conference |
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Abbreviated title | BMEiCON 2018 |
Country/Territory | Thailand |
City | Chiang Mai |
Period | 21/11/18 → … |
Keywords
- Deep Learning
- Medical Imaging
- PET/CT
- Segmentation
- Urinary Bladder
ASJC Scopus subject areas
- Artificial Intelligence
- Instrumentation
- Biomedical Engineering
Activities
- 1 Talk at conference or symposium
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PET-Train: Automatic Ground Truth Generation from PET Acquisitions for Urinary Bladder Segmentation in CT Images using Deep Learning
Antonio Pepe (Speaker)
2018Activity: Talk or presentation › Talk at conference or symposium › Science to science