Robust GPU-based Virtual Reality Simulation of Radio Frequency Ablations for Various Needle Geometries and Locations

Purpose: Radio-frequency ablations play an important role in the therapy of malignant liver lesions. The navigation of a needle to the lesion poses a challenge for both the trainees and intervening physicians. Methods: This publication presents a new GPU-based, accurate method for the simulation of radio-frequency ablations for lesions at the needle tip in general and for an existing visuo-haptic 4D VR simulator. The method is implemented real-time capable with Nvidia CUDA. Results: It performs better than a literature method concerning the theoretical characteristic of monotonic convergence of the bioheat PDE and a in vitro gold standard with significant improvements (p < 0.05) in terms of Pearson correlations. It shows no failure modes or theoretically inconsistent individual simulation results after the initial phase of 10 seconds. On the Nvidia 1080 Ti GPU it achieves a very high frame rendering performance of >480 Hz. Conclusion: Our method provides a more robust and safer real-time ablation planning and intraoperative guidance technique, especially avoiding the over-estimation of the ablated tissue death zone, which is risky for the patient in terms of tumor recurrence. Future in vitro measurements and optimization shall further improve the conservative estimate.Comment: 18 pages, 14 figures, 1 table, 2 algorithms, 2 movie

Quantitative and Qualitative Evaluation of Transforming to Flipped-Classroom from Instruction Teaching using Micro Feedback

Recently, the institutionalized transformation of frontal instruction classrooms into active learning spaces to foster the concept of (inter-)active learning has gained increasing attention. To investigate the impact of elements of active learning on learning reception of students in an advanced small sized MSc STEM course (<25 students), a traditional instructor teaching style class was transformed to flipped-classroom teaching. Before and after each lecture, anonymized evaluation Likert items from the students were recorded. Thus, both teaching styles for every given lecture were covered equally. In both classrooms, some didactic and methodological elements were kept constant, while others were changed when flipped-classroom took over semester midterm. Qualitative and quantitative results indicated that the flipped-classroom format generated greater learning effects as well as classroom enjoyment, fostered students’ self-regulated learning, enhanced group interaction, stimulated group activity and guaranteed a more synergistic learning behavior

3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.Comment: 10 pages, 5 figures, 1 tabl

Comparison of 2D vs. 3D Unet Organ Segmentation in abdominal 3D CT images

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. Firsteach relevant organ’s volume of interest is extracted as bounding box. The extracted volume acts as input for asecond stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmen-tation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial resultsindicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instancewere tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal keyorgans, there were no significant differences, but we observe highly significant advantages for the 2D U-Net interms of GPU computational efforts for all organs under study