The CAMP Lab Computer Aided Medical Procedures and Augmented Reality

Abstract-The CAMP lab is integrated within the Department of Informatics at Technical University of Munich and is considered one of the leading groups concerned with medical augmented reality, computer assisted interventions, as well as non-medical related computer vision. In this short paper, we give an outline of the history of the lab and present a summary of some of our past and current activities relevant to augmented and virtual reality in computer assisted interventions and surgeries. References to published work in major journals and conferences allow the reader to get access to more detailed information on each subject. It was not possible to cover all aspects of our research within this paper, but we hope to provide an overview on some of these within this short paper. The readers are also invited to visit our web-site at http://campar.in.tum.de to get more information on aspects of our work. Applications for PhD and PostDoc positions can be made through the form a

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting

Template-based CTA x-ray angio rigid registration of coronary arteries in frequency domain

This study performs 3D to 2D rigid registration of segmented pre-operative CTA coronary arteries with a single segmented intra-operative X-ray Angio frame in both frequency and spatial domains for real-time Angiography interventions by C-arm fluoroscopy. Most of the work on rigid registration in literature required a close initialization of poses and/or positions because of the abundance of local minima and high complexity that searching algorithms face. This study avoids such setbacks by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. First, template DRRs as candidate poses of 3D vessels of segmented CTA are produced by rotating the camera (image intensifier) around the DICOM angle values with a wide range as in C-arm setup. We have compared the 3D poses of template DRRs with the real X-ray after equalizing the scales (due to disparities in focal length distances) in 3 domains, namely Fourier magnitude, Fourier phase and Fourier polar. The best pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that these methods are robust against noise and occlusion which was also validated by our results. Translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of our objective function without local minima due to distance maps. Final results were evaluated in 2D projection space rather than with actual values in 3D due to lack of ground truth, ill-posedness of the problem which we intend to address in future

Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy. Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study. Results: Final results were evaluated in 2D projection space for patient data; and with ground truth values and landmark distances for the images acquired with a solid phantom vessel. Results validate that rotation recovery in frequency domain is robust against differences in segmentations in two modalities. Distance-map translation is successful in aligning coronary trees with highest possible overlap. Conclusions: Numerical and qualitative results show that single view rigid alignment in projection space is successful. This work can be extended with multiple views to resolve depth ambiguity and with deformable registration to account for nonrigid motion in patient data. (c) 2013 American Association of Physicists in Medicine