Current laser surgery on vocal chords requires the patient to be under general anaesthesia due to relatively low cutting speed and precision. Even minor surgeries can change vocal properties, requiring lengthy post-operative therapy. To solve this problem and reduce recovery time we propose a laryngoscope capable of performing the surgery while the patient is awake. To realize this, it is necessary for each cut to be made on the shortest time scale with the highest precision possible. It is also important to have high speed feedback to initiate or terminate the cutting process as well as to maintain the proper cutting position. In this laryngoscope we employ a coaxial MHz OCT and laser cutting system with a MEMS galvo scanner combined with a high speed stereo camera set. The MHz OCT is responsible for axial feedback and measuring the depth of cut while the stereo camera set is used to adjust the MEMS scanner for lateral offsets. We have determined the optimal optical layout for the laryngoscope using Zemax and have developed 3D CAD models of the prototype demonstrator prior to fabrication and assembly. This new laryngoscope could make laser cuts up to 50% smaller in width than traditional multimode fiber based cuts, in addition to reducing overall surgery time and increasing the precision of each cut.
KEYWORDS: Denoising, Optical coherence tomography, Signal to noise ratio, Image classification, Speckle, Medical imaging, Wavelets, Medical diagnostics, Ophthalmology, Classification systems
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease characteristics on retinal optical coherence tomography images in ophthalmology is presented. We propose semantic denoising autoencoders, which combine a convolutional denoising autoencoder with a priorly trained ResNet image classifier as regularizer during training. This promotes the perceptibility of delicate details in the denoised images that are important for diagnosis and filters out only informationless background noise. With our approach, higher peak signal-to-noise ratios with PSNR = 31.0 dB and higher classification performance of F1 = 0.92 can be achieved for denoised images compared to state-of-the-art denoising. It is shown that semantically regularized autoencoders are capable of denoising retinal OCT images without blurring details of diseases.
In microsurgery, lasers have emerged as precise tools for bone ablation. A challenge is automatic control of laser bone ablation with 4D optical coherence tomography (OCT). OCT as high resolution imaging modality provides volumetric images of tissue and foresees information of bone position and orientation (pose) as well as thickness. However, existing approaches for OCT based laser ablation control rely on external tracking systems or invasively ablated artificial landmarks for tracking the pose of the OCT probe relative to the tissue. This can be superseded by estimating the scene flow caused by relative movement between OCT-based laser ablation system and patient. Therefore, this paper deals with 2.5D scene flow estimation of volumetric OCT images for application in laser ablation. We present a semi-supervised convolutional neural network based tracking scheme for subsequent 3D OCT volumes and apply it to a realistic semi-synthetic data set of ex vivo human temporal bone specimen. The scene flow is estimated in a two-stage approach. In the first stage, 2D lateral scene flow is computed on census-transformed en-face arguments-of-maximum intensity projections. Subsequent to this, the projections are warped by predicted lateral flow and 1D depth flow is estimated. The neural network is trained semi-supervised by combining error to ground truth and the reconstruction error of warped images with assumptions of spatial flow smoothness. Quantitative evaluation reveals a mean endpoint error of (4.7 ± 3.5) voxel or (27.5 ± 20.5) μm for scene flow estimation caused by simulated relative movement between the OCT probe and bone. The scene flow estimation for 4D OCT enables its use for markerless tracking of mastoid bone structures for image guidance in general, and automated laser ablation control.
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