Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees

Bier, Bastian; Aschoff, Katharina; Syben, Christopher; Unberath, Mathias; Levenston, Marc E.; Gold, Garry E.; Fahrig, Rebecca; Maier, Andreas

doi:10.1007/978-3-030-00129-2_10

Cited by 17 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [120], Zhang et al demonstrate this effectively for metal artifacts. As a last example, we list Bier et al here, as they show that deep learning-based motion tracking is also feasible for motion compensated reconstruction [121].…”

Section: Image Reconstructionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

Self Cite

441

224

View full text Add to dashboard Cite

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

show abstract

Section: Image Reconstructionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

Self Cite

441

224

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm of keypoint detection results in a decent accuracy, similar to [39,40]. Given the troublesome characteristics of images, we believe it is a success.…”

Section: Discussionmentioning

confidence: 84%

Detection of Pediatric Femur Configuration on X-ray Images

Drazkowska

2021

Applied Sciences

View full text Add to dashboard Cite

In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.

show abstract

“…Deep learning has high potential to overcome some of those limitations by replacing bottlenecks of traditional methods with data-driven algorithms. For example, Bier et al [42] tackled the problem of manual marker placement by learning anatomical landmarks directly from the projection images. The presented cGAN-based approaches potentially have the risk of vanishing anatomical malformations, however, they may solve the chicken-egg problem for marker-free registration approaches.…”

Section: Potentials and Limitations In The State Of The Artmentioning

confidence: 99%

Appearance Learning for Image-Based Motion Estimation in Tomography

Preuhs

Manhart

Roser

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Geometric information within this process is usually depending on the system setting only, i. e., the scanner position or readout direction. Patient motion therefore corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object. To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach. The RPE measures the motioninduced geometric deviations independent of the object based on virtual marker positions, which are available during training. We train our network using 27 patients and deploy a 21-4-2 split for training, validation and testing. In average, we achieve a residual mean RPE of 0.013 mm with an inter-patient standard deviation of 0.022 mm. This is twice the accuracy compared to previously published results. In a motion estimation benchmark the proposed approach achieves superior results in comparison with two state-of-the-art measures in nine out of twelve experiments. The clinical applicability of the proposed method is demonstrated on a motion-affected clinical dataset.

show abstract

Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees

Cited by 17 publications

References 13 publications

A gentle introduction to deep learning in medical image processing

A gentle introduction to deep learning in medical image processing

Detection of Pediatric Femur Configuration on X-ray Images

Appearance Learning for Image-Based Motion Estimation in Tomography

Contact Info

Product

Resources

About