Birgit Pfarrkirchner

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

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...

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...

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...

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...

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...

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...

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...

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...