Index
AI-Driven Monuments Identification System and its details
In the realm of computer vision, significant research is currently dedicated to object detection
and recognition. Research groups and developers are actively striving to enhance machine
learning solutions, aiming to boost the accuracy of image detection and recognition in
accordance with specific use cases. The German AI-driven Monuments Detection System is an
innovative project aimed at providing tourists with an enhanced experience by leveraging
artificial intelligence (AI) to recognize and provide historical information about prominent
monuments in Germany. This document presents the project’s methodology, results, and
implications. With this machine, a user can scan the historical place and view its historical
details. There are five categories used in this model. Python is used as a programming
language with the TensorFlow framework.
Volumetric Cerebral Vessel Labeling
Transferring of emotional knowledge: from quantitative emotions to qualitative emotions
X-ray image synthesis via an open source framework
Use a new open source framework generating X-ray images for deep learning model training.
Requirement: Python, CT reconstruction
Please attach your CV and transcripts to fuxin.fan@fau.de
Geometric Domain Adaptation for CBCT Segmentation
In this project, we perform a computational domain transfer to introduce cone-beam artifacts to the training data. We evaluate its impact on the results of supervised training for the segmentation of the lungs. For this, already labeled CT volumes are reconstructed to artificial CBCT volumes without a complex deep learning-based method, like introduced by Jia X et al.,5 but rather by computational reconstruction. The purpose is to have a network for stable segmentation on real CBCT volumes. A major advantage of our approach is that the artificial
CBCT volumes can not only be computed easily from thoracic CT volumes but also the pixel-wise segmentation can be re-used without putting in the great effort of labeling. This allows for supervised training.
Realistic Simulation of Collimated X-Ray images for Collimator Edge Segmentation using Deep Learning
Collimator detection in X-ray systems has long posed a challenge, particularly when information about the detector’s position relative to the source is either unreliable or completely unavailable. In this paper [1], we introduce a physically motivated image processing pipeline designed to simulate the intricate characteristics of collimator shadows in X-ray images. The primary objective of this pipeline is to address the scarcity of training data for deep neural networks, which are increasingly promising for collimator detection. By applying the pipeline to deep networks initially limited by small datasets, our approach equips them with the necessary information to learn and generalize effectively.
Our pipeline is a comprehensive solution that leverages several key components to
generate realistic collimator images. Employing randomized labels to describe collimator shapes and their respective locations ensures diversity and representativeness. In addition, we integrate a convolution kernel based scattered radiation simulation mechanism, which is a crucial factor in real-world X-ray imaging. To complete the simulation process, we introduce Poisson noise to replicate the inherent characteristics of collimator shadows in X-ray images.
Comparing the simulated data with real collimator shadows demonstrates the authenticity of our approach and its potential to bridge the gap between synthetic and real-world data. Moreover, incorporating simulated data into our deep learning framework not only serves as a valid substitute for real collimators but also significantly improves generalization in real-world applications, holding great promise for the field of collimator detection.
This work was presented at the DALI workshop at the MICCAI conference in Vancouver, Canada and was published in the proceedings:
1. El-Zein B, Eckert D, Weber T, Rohleder M, Ritschl L, Kappler S et al. A Realistic Collimated
X-Ray Image Simulation Pipeline. Data Augmentation, Labelling, and Imperfections – Third
MICCAIWorkshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, October
12, 2023, Proceedings. Springer Nature. 2023, (in press)