X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

Bier, Bastian; Unberath, Mathias; Zaech, Jan-Nico; Fotouhi, Javad; Armand, Mehran; Osgood, Greg; Navab, Nassir; Maier, Andreas

doi:10.1007/978-3-030-00937-3_7

Cited by 84 publications

(74 citation statements)

References 15 publications

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“…In terms of structure and organization, we follow [22] here, but add recent developments in physical simulation and image reconstruction. [71] and the X-ray transform-invariant landmark detection by Bier et al [67] (projection image courtesy of Dr. Unberath). The right hand side shows a U-net-based stent segmentation after Breininger et al [72].…”

Section: Resultsmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

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417

224

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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.

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Section: Resultsmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

Self Cite

417

224

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“…3. Anatomical landmark detection on real data of cadaveric specimen using the method detailed in [9]. pixel byĒ(u) and estimate quantum noise asĒ/N0(p P oisson (N ) − N ), where p P oisson is the Poisson generating function.…”

Section: Deepdrrmentioning

confidence: 99%

DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures

Unberath

Zaech

Lee

et al. 2018

Lecture Notes in Computer Science

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Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology. Interventional radiology, however, has not yet benefited substantially from the advent of deep learning, in particular because of two reasons: 1) Most images acquired during the procedure are never archived and are thus not available for learning, and 2) even if they were available, annotations would be a severe challenge due to the vast amounts of data. When considering fluoroscopy-guided procedures, an interesting alternative to true interventional fluoroscopy is in silico simulation of the procedure from 3D diagnostic CT. In this case, labeling is comparably easy and potentially readily available, yet, the appropriateness of resulting synthetic data is dependent on the forward model. In this work, we propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy and digital radiography from CT scans, tightly integrated with the software platforms native to deep learning. We use machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, combined with analytic forward projection and noise injection to achieve the required performance. On the example of anatomical landmark detection in X-ray images of the pelvis, we demonstrate that machine learning models trained on DeepDRRs generalize to unseen clinically acquired data without the need for re-training or domain adaptation. Our results are promising and promote the establishment of machine learning in fluoroscopy-guided procedures.

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“…Random HU intensities roughly corresponding to muscle tissue are used for any voxel that no longer corresponds to the fragment or femur. The muscle intensities are randomly drawn from N (α, 20) for each voxel location; α is drawn from U [35,55] for each pair of fragment and femur relocations.…”

Section: Simulation Studymentioning

confidence: 99%

Pose Estimation of Periacetabular Osteotomy Fragments With Intraoperative X-Ray Navigation

Grupp

Hegeman

Murphy

et al. 2020

IEEE Trans. Biomed. Eng.

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Objective: State of the art navigation systems for pelvic osteotomies use optical systems with external fiducials. We propose the use of X-Ray navigation for pose estimation of periacetabular fragments without fiducials. Methods: A 2D/3D registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and 6 cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan is refined to more accurately match the intraoperative shape. Results: In simulation, average fragment pose errors were 1.3°/1.7 mm when the planned fragment matched the intraoperative fragment, 2.2°/2.1 mm when the plan was not updated to match the true shape, and 1.9°/2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2°/2.2 mm, 3.8°/2.5 mm, and 3.5°/2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 2°for all fragment shapes in simulation and cadaver experiments. Conclusion: The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability. Significance: Human interpretation of fragment

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X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

Cited by 84 publications

References 15 publications

A gentle introduction to deep learning in medical image processing

A gentle introduction to deep learning in medical image processing

DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures

Pose Estimation of Periacetabular Osteotomy Fragments With Intraoperative X-Ray Navigation

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