Dr. Jürgen Walter Jenne Profile

Dr. Jürgen Walter Jenne

at Fraunhofer MEVIS

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Author

Publications (2)

Proceedings Article | 4 April 2022 Poster + Paper

LSTM-U-net for the robust segmentation of veins in ultrasound sequences

Daniel Mensing, Johannes Gregori, Jürgen Jenne, Michael Stritt, Björn Gerold, Matthias Günther

Proceedings Volume 12034, 120341R (2022) https://doi.org/10.1117/12.2608085

KEYWORDS: Image segmentation, Veins, Ultrasonography, Neural networks, Computer programming, Network architectures, Target detection, Diagnostics, Convolution

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Varicose veins are classified as a chronic venous disease of which almost a quarter of the population of the U.S suffers from.1 Although most cases only develop mild symptoms, 6% of the affected women and men between 40 and 80 years develop signs of chronic vein insufficiency like venous ulceration.2 The number of these patients is two million in the U.S. alone. Treatment of varicose veins was mostly composed of surgical interventions until thermal endovenous ablation was introduced3 which resulted in lower cost and faster recovery of the patient.2 A new completely non-invasive method is High-Intensity Focused Ultrasound (HIFU) in which an ultrasound pulse is applied from outside the skin surface in order to thermally ablate the vein and close it permanently.3 This method relies heavily on diagnostic imaging through ultrasound to detect the target vein for ablation and to guide and monitor the procedure. An automated approach to detect and localize the vein during the treatment is rational because of the tedious work to follow the vessel in transversal direction. Previous works in the field of vessel segmentation in ultrasound images with deep learning focus on the frame-wise segmentation of the vessel.4 The possibility of further improvement of this method can be achieved by leveraging the temporal information about the location of the vessel. A previous work proposed by Mathai et. al.5 also features a U-net which implements LSTM-layers in the decoder part of the network and is used for the segmentation of vessels in ultrasound images. The segmentation of ultrasound image sequences can be combined with the prediction of segmentations of future frames to improve the predictive capacity of the model. Zhao et. al. proposed to use a ConvLSTM to predict future frames of ultrasound images for tongue movement,6 which was successful in predicting the next ultrasound image for a sequence of eight frames. In this work we propose a deep learning method for the localization and segmentation of veins in ultrasound sequences in combination with the prediction of future vessel segmentations for the automation of HIFU ablation treatments.

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Deep learning-assisted fully automatic fiber tracking for tremor treatment

Annika Hänsch, Jürgen Jenne, Neeraj Upadhyay, Carsten Schmeel, Veronika Purrer, Ullrich Wüllner, Jan Klein

Proceedings Volume 12034, 120342A (2022) https://doi.org/10.1117/12.2611710

KEYWORDS: Image segmentation, Image registration, Brain, Diffusion tensor imaging, Magnetic resonance imaging

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