Feasibility of a markerless tracking system based on optical coherence tomography

Clinical tracking systems are popular but typically require specific tracking markers. During the last years, scanning speed of optical coherence tomography (OCT) has increased to A-scan rates above 1 MHz allowing to acquire volume scans of moving objects. Thorefore, we propose a markerless tracking system based on OCT to obtain small volumetric images including information of sub-surface structures at high spatio-temporal resolution. In contrast to conventional vision based approaches, this allows identifying natural landmarks even for smooth and homogeneous surfaces. We describe the optomechanical setup and process flow to evaluate OCT volumes for translations and accordingly adjust the position of the field-of-view to follow moving samples. While our current setup is still preliminary, we demonstrate tracking of motion transversal to the OCT beam of up to 20 mm/s with errors around 0.2 mm and even better for some scenarios. Tracking is evaluated on a clearly structured and on a homogeneous phantom as well as on actual tissue samples. The results show that OCT is promising for fast and precise tracking of smooth, monochromatic objects in medical scenarios.Comment: Accepted at SPIE Medical Imaging 201

Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models

The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, machine learning methods using dermoscopic images have been proposed to assist human experts. Other approaches have studied electrical impedance spectroscopy (EIS) as a basis for clinical decision support systems. Both methods represent different ways of measuring skin lesion properties as dermoscopy relies on visible light and EIS uses electric currents. Thus, the two methods might carry complementary features for lesion classification. Therefore, we propose joint deep learning models considering both EIS and dermoscopy for melanoma detection. For this purpose, we first study machine learning methods for EIS that incorporate domain knowledge and previously used heuristics into the design process. As a result, we propose a recurrent model with state-max-pooling which automatically learns the relevance of different EIS measurements. Second, we combine this new model with different convolutional neural networks that process dermoscopic images. We study ensembling approaches and also propose a cross-attention module guiding information exchange between the EIS and dermoscopy model. In general, combinations of EIS and dermoscopy clearly outperform models that only use either EIS or dermoscopy. We show that our attention-based, combined model outperforms other models with specificities of 34.4% (CI 31.3-38.4), 34.7% (CI 31.0-38.8) and 53.7% (CI 50.1-57.6) for dermoscopy, EIS and the combined model, respectively, at a clinically relevant sensitivity of 98%.Comment: Accepted at SPIE Medical Imaging 202

Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods

Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is an invasive biopsy. Recently, non-invasive imaging techniques such as confocal laser microscopy and optical coherence tomography (OCT) have been used for tissue assessment. In particular, in a recent study experts classified lesions in the UADT with respect to their invasiveness using OCT images only. As the results were promising, automatic classification of lesions might be feasible which could assist experts in their decision making. Therefore, we address the problem of automatic lesion classification from OCT images. This task is very challenging as the available dataset is extremely small and the data quality is limited. However, as similar issues are typical in many clinical scenarios we study to what extent deep learning approaches can still be trained and used for decision support.Comment: Accepted for publication at CARS 201

A Deep Learning Approach for Motion Forecasting Using 4D OCT Data

Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or motion compensation. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution. Recently, deep learning methods have shown promising performance for OCT-based motion estimation based on two volumetric images. We extend this approach and investigate whether using a time series of volumes enables motion forecasting. We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes. We design and evaluate five different 3D and 4D deep learning methods using a tissue data set. Our best performing 4D method achieves motion forecasting with an overall average correlation coefficient of 97.41%, while also improving motion estimation performance by a factor of 2.5 compared to a previous 3D approach.Comment: Accepted for publication at MIDL 2020: https://openreview.net/forum?id=WVd56kgR

Deep Learning for High Speed Optical Coherence Elastography

Mechanical properties of tissue provide valuable information for identifying lesions. One approach to obtain quantitative estimates of elastic properties is shear wave elastography with optical coherence elastography (OCE). However, given the shear wave velocity, it is still difficult to estimate elastic properties. Hence, we propose deep learning to directly predict elastic tissue properties from OCE data. We acquire 2D images with a frame rate of 30 kHz and use convolutional neural networks to predict gelatin concentration, which we use as a surrogate for tissue elasticity. We compare our deep learning approach to predictions from conventional regression models, using the shear wave velocity as a feature. Mean absolut prediction errors for the conventional approaches range from 1.32$\pm$0.98 p.p. to 1.57$\pm$1.30 p.p. whereas we report an error of 0.90$\pm$0.84 p.p for the convolutional neural network with 3D spatio-temporal input. Our results indicate that deep learning on spatio-temporal data outperforms elastography based on explicit shear wave velocity estimation.Comment: Accepted at IEEE International Symposium on Biomedical Imaging 202