Automated geometric features evaluation method for normal foot skeleton model

Normal foot model is a geometric model of a healthy human foot. As the comparison of the processed feet requires a reference ideal healthy foot parameterization it was necessary to create such a model by defining skeleton geometric features and generating the feature set on a dataset population. Manual positioning of such number of landmarks is both a complex and time consuming task for a skilled radiologist, not to mention the total cost of such a procedure. Thus it was recommended to formulate an automated computer algorithm to perform this procedure with accuracy at a comparable level as the manual process. The following paper describes our approach based on automatic landmark positioning in a volumetric foot dataset. The proposed automated procedure is based on four main steps: manual landmark positioning on a reference dataset, registration of the reference dataset with the examined study, transformation of landmark positions from the reference dataset space into the examined dataset space, and calculation of the geometric features on the basis of landmarks positions. The results of our algorithm are presented and discussed in the context of pros and cons of the automated method itself as well as in the context of the generated normal foot model

Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks

Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our models are trained and validated on an acquired dataset of over 250.000 sagittal and over 450.000 axial ultrasound slices. The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress. We also observe that parameters associated with i.a. intratendinous healing processes are better modeled with sagittal slices. We prove that ultrasound imaging is quantitatively useful for clinical assessment of Achilles tendon healing process and should be viewed as complementary to magnetic resonance imaging.Comment: Paper accepted to MICCAI'19 SUSI worksho

VisNow – a Modular, Extensible Visual Analysis Platform

A new, dataflow driven, modular visual data analysis platform with extensive data processing and visualization capabilities is presented. VisNow is written in Java, easily extendable to incorporate new modules and module libraries. Dataflow networks built with the help of interactive network editor can be wrapped into stand-alone application for the end users

A Weighted Stochastic Conjugate Direction Algorithm for Quantitative Magnetic Resonance Images—A Pattern in Ruptured Achilles Tendon T2-Mapping Assessment

This study presents an accurate biexponential weighted stochastic conjugate direction (WSCD) method for the quantitative T2-mapping reconstruction of magnetic resonance images (MRIs), and this approach was compared with the non-negative-least-squares Gauss–Newton (GN) numerical optimization method in terms of accuracy and goodness of fit of the reconstructed images from simulated data and ruptured Achilles tendon (AT) MRIs. Reconstructions with WSCD and GN were obtained from data simulating the signal intensity from biexponential decay and from 58 MR studies of postrupture, surgically repaired ATs. Both methods were assessed in terms of accuracy (closeness of the means of calculated and true simulated T2 values) and goodness of fit (magnitude of mean squared error (MSE)). The lack of significant deviation in correct T2 values for the WSCD method was demonstrated for SNR ≥ 20 and for GN–SNR ≥ 380. The MSEs for WSCD and GN were 287.52 ± 224.11 and 2553.91 ± 1932.31, respectively. The WSCD reconstruction method was better than the GN method in terms of accuracy and goodness of fit

Estimating Achilles tendon healing progress with convolutional neural networks

Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorder in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts. In this paper, we propose to significantly reduce the complexity of this assessment using a novel method based on a pre-trained convolutional neural network. We first train our neural network on over 500,000 2D axial cross-sections from over 3000 3D MRI studies to classify MRI images as belonging to a healthy or injured class, depending on the patient's condition. We then take the outputs of modified pre-trained network and apply linear regression on the PCA-reduced space of the features to assess treatment progress. Our method allows to reduce up to 5-fold the amount of data needed to be registered during the MRI scan without any information loss. Furthermore, we are able to predict the healing process phase with equal accuracy to human experts in 3 out of 6 main criteria. Finally, contrary to the current approaches to regeneration assessment that rely on radiologist subjective opinion, our method allows to objectively compare different treatments methods which can lead to improved diagnostics and patient's recovery.Comment: Paper accepted to MICCAI'1

Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful?

The aim of this study was to verify improved, ensemble-based strategy for inferencing with use of our solution for quantitative assessment of tendons and ligaments healing process and to show possible applications of the method. Methods: We chose the problem of the Achilles tendon rupture as an example representing a group of common sport traumas. We derived our dataset from 90 individuals and divided it into two subsets: healthy individuals and patients with complete Achilles tendon ruptures. We computed approx. 160 000 2D axial cross-sections from 3D MRI studies and preprocessed them to create a suitable input for artificial intelligence methods. Finally, we compared different training methods for chosen approaches for quantitative assessment of tendon tissue healing with the use of statistical analysis. Results: We showed improvement in inferencing with use of the ensemble technique that results from achieving comparable accuracy of 99% for our previously published method trained on 500 000 samples and for the new ensemble technique trained on 160 000 samples. We also showed real-life applications of our approach that address several clinical problems: (1) automatic classification of healthy and injured tendons, (2) assessment of the healing process, (3) a pathologic tissue localization. Conclusions: The presented method enables acquiring comparable accuracy with less training samples. The applications of the method presented in the paper as case studies can facilitate evaluation of the healing process and comparing with previous examination of the same patient as well as with other patients. This approach might be probably transferred to other musculoskeletal tissues and joints

On-Site Computed Tomography Versus Angiography Alone to Guide Coronary Stent Implantation: A Prospective Randomized Study

OBJECTIVES: The effect of intraprocedural coronary computed tomography angiography (coronary CTA) guidance on percutaneous coronary intervention (PCI) is unknown. We sought to determine the influence of CTA guidance on procedural strategies and immediate angiographic outcomes of PCI. METHODS: Sixty patients were randomized to CTA-guided PCI (29 patients, 36 lesions) or angiography-guided PCI (31 patients, 39 lesions). To enable hands-free manipulation of CTA images by the interventional cardiologist during PCI, we developed an onsite augmented-reality (AR) system comprising a mobile application and AR glass. The primary endpoints were defined as: (1) stent length; and (2) largest stent diameter according to compliance chart. Procedural strategies, two-dimensional (2D) and three-dimensional (3D) quantitative coronary angiography (QCA), and safety outcomes were compared. RESULTS: Whereas CTA guidance resulted in significantly higher frequency of stent postdilation using non-compliant (67% vs 31%; P<.01) and shorter balloons (16.6 ± 5.4 mm vs 20.5 ± 9.4 mm; P=.04) with numerically larger diameter (3.50 ± 0.63 mm vs 3.28 ± 0.45 mm; P=.10), it did not differ from angiography guidance with respect to lesion predilation, stent length, largest stent diameter according to compliance chart, and nominal stent diameter. The results of 2D- and 3D-QCA and safety outcomes were similar between groups. Neither death nor stroke occurred in either group. CONCLUSIONS: PCI under intraprocedural CTA guidance is associated with similar stent size selection and more frequent stent postdilation, resulting in comparable immediate angiographic and safety outcomes as compared with PCI under angiographic guidance alone