Birgit Pfarrkirchner

Birgit Pfarrkirchner
Graz University of Technology | TU Graz · Institute of Computer Graphics and Knowledge Visualisation

About

9
Publications
3,264
Reads
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45
Citations

Publications

Publications (9)
Poster
Full-text available
Deep learning with neural networks is an increasingly important topic for research and economic purposes. Software giants use deep networks for the development of their latest technological gadgets. Daily examples are Facebook’s face detection, Apple’s speech recognition Siri or Google Translate, which all comprise deep learning algorithms. The mo...
Conference Paper
This contribution presents the automatic segmentation of the lower jawbone (mandible) in humans’ computed tomography (CT) images with the support of trained deep learning networks. CT acquisitions from the mandible frequently include radiological artifacts e.g. from metal dental restorations, ostheosynthesis materials or include trauma related free...
Conference Paper
Full-text available
Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neit...
Poster
Full-text available
Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neit...
Conference Paper
Accurate segmentation of medical images is a key step in medical image processing. As the amount of medical images obtained in diagnostics, clinical studies and treatment planning increases, automatic segmentation algorithms become increasingly more important. Therefore, we plan to develop an automatic segmentation approach for the urinary bladder...
Conference Paper
Full-text available
The lower jawbone data preparation for deep learning is proposed. In ten cases, surgeons segmented the lower jawbone in each slice to generate the ground truth. Since the number of present images was deemed insufficient to train a deep neural network, data was augmented with geometric transformations and added noise. Flipping, rotating and scaling...
Poster
Full-text available
Segmentation is an important branch in medical image processing and the basis for further detailed investigations on computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound (US) or nuclear images. Through segmentation, an image is divided into various connected areas that correspond to certain tissue types. A common aim is to...
Poster
Full-text available
Accurate segmentation of medical images is a key step in medical image processing. As the amount of medical images obtained in diagnostics, clinical studies and treatment planning increases, automatic segmentation algorithms become increasingly more important. Therefore, we plan to develop an automatic segmentation approach for the urinary bladder...

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Projects

Project (1)
Project
The objective of this interdisciplinary project between Computer Science and Medicine is to test and evaluate Deep Learning Tools and Frameworks for the automatic analysis of medical data. On one hand, this could be an automatic detection of abnormalities in patient scans. On the other hand, this could be a precise segmentation of a pathology.