We developed a novel high resolution 3D ultrasound B-scan (3D-UBS) imaging system that provides automated 3D acquisition and easily interpretable, interactive 3D visualization, including en-face and oblique views of the whole eye. Early and accurate diagnosis of ocular trauma and other associated injuries is essential for future prevention of complications. Conventional 2D ultrasound is limited in its use due to lack of trained ultrasonographer at point-of-care, difficulty in finding the optimal imaging plane and lack of anatomical context for easy interpretation. 2D hand-held ultrasound is also limited in case of perforated globes. Computed tomography (CT) is expensive and cannot be utilized if perforating intra-ocular foreign bodies (IOFBs) are small, non-metallic, or organic in nature. This study aimed to address the unmet clinical need for advanced 3D visualization of IOFBs and ocular injuries with 3D-UBS. We imaged porcine eye models for IOFBs. 3D-UBS enabled easily-obtained, informative images of ocular injuries, without an expert as required in conventional 2D ultrasound. En face and oblique views provided by multiplanar reformatting allows selection of optimal planes after acquisition. Size and shape of the IOFBs can be detected more accurately with 3D-UBS. 3D-UBS also provides information on location of IOFBs with respect to other important ocular structures. 3D-UBS shows 2.4 times contrast improvement compared to CT in wooden IOFB visualization. Our study demonstrated that novel 3D-UBS can be used for assessing ocular injuries (i.e., identifying the location, size, and shape of IOFBs) which can guide the treatment process.
Advances in three-dimensional (3D) microscopy are providing never-before-seen images of coronary microvasculature organization. However, it remains inaccessible to researchers due to difficult sample preparation and image analysis. We present a deep learning network that can segment the coronary microvasculature in 3D microscopy without vessel staining. The network is based on 3D U-net and accepts DAPI (nuclei) and autofluorescence (tissue structure) volumes as inputs. The network detects vessels with high accuracy when compared to the ground truth obtained from isolectin staining. Contrast-free segmentation of vessels simplifies sample preparation, frees fluorescent channels during imaging and opens the door toward user-friendly 3D microscopy.
We developed a 3D ultrasound biomicroscopy (3D-UBM) imaging system and used it to assess ciliary tissues in the eye. As ultrasound can penetrate opaque ocular tissues, 3D-UBM has a unique ability to creating informative 3D visualization of anterior ocular structures not visible with other, optical imaging modalities. Ciliary body, located behind the iris, is responsible for fluid production making it an important ocular structure for glaucoma. Only 3DUBM allows visualization and measurements of ciliary body. Several steps were required for visualization and quantitative assessment. To reduce eye motion in 3D-UBM volumes, we performed slice alignment using Transformation Diffusion approach to avoid geometric artifacts. We applied noise reduction and aligned the volumes to the optic axis to create 3D renderings of ciliary body in its entirety. We extracted two different sets of images from these volumes, namely en face and radial images. We created a dataset of eye volumes with slices containing ciliary body, segmented by two analyst trainees and approved by two experts. Deep learning segmentation models (UNet and Inception-v3+) were trained on both sets of images using appropriate loss functions. Using en face images and Inception-v3+, and weighted cross entropy loss, we obtained Dice = 0.81±0.04. Using radial images, Inception-v3+, and with Dice loss, results were improved to Dice = 0.89±0.03, probably because radial images enable full usage of the symmetry of the eye. Cyclophotocoagulation (CPC) is a glaucoma treatment that is used to destroy the ciliary body partially or completely and reduce fluid production. 3D-UBM allows one to visualize and quantitatively analyze CPC treatments.
Advances in tissue clearing and three-dimensional microscopy require new tools to analyze the resulting large volumes with single-cell resolution. Many existing nuclei detection approaches fail when applied to the developing heart, with its high cell density, and elongated myocytes. We propose a new regression-based convolutional neural network that detect nuclei centroids in whole DAPI-stained embryonic quail hearts. High nuclei detection accuracy was obtained in two different hearts where our algorithm outperformed other deep learning approaches. Once nuclei were identified we were also able to extract properties such as orientation and size, which enables future studies of heart development and disease.
High frequency ultrasound biomicroscopy (UBM) images are used in clinical ophthalmology due to its ability to penetrate opaque tissues and create high resolution images of deeper intraocular structures. Because these inexpensive, high frequency (50 MHz) systems use single ultrasound elements, there is a limitation in visualizing small structures and anatomical landmarks, especially outside focal area, due to the lack of dynamic focusing. The wide and axially variant point spread function degrade image quality and obscure smaller structures. We created a fast, generative adversarial network (GAN) method to apply axially varying deconvolution for our 3D ultrasound biomicroscopy (3D-UBM) imaging system. Original images are enhanced using a computationally expensive axially varying deconvolution, giving paired original and enhanced images for GAN training. Supervised generative adversarial networks (pix2pix) were trained to generate enhanced images from originals. We obtained good performance metrics (SSIM = 0.85 and PSNR = 31.32 dB) in test images without any noticeable artifacts. GAN deconvolution runs at about 31 msec per frame on a standard graphics card, indicating that near real time enhancement is possible. With GAN enhancement, important ocular structures are made more visible.
In addition to structural morphology, tissue’s vascular network may provide valuable complementary information on the altered lesions and the tumor angiogenesis. Although ultrafast Doppler ultrasound (UDF) imaging enables ultrasound to image microvessels with high sensitivity, these images still suffer from artifacts. In this study, we addressed small vessel visualization and associated noise problem in ultrasound high framerate plane wave in-vivo imaging. We developed a combination of nonlocal means and morphological filtering on the UDF clutter removed data in order to obtain enhanced vessel images and improved outlining. We tested our algorithm on a flow phantom and in vivo data of the breast masses. The results show that the proposed method added an incremental gain of about 16 dB in terms of signal to noise ratio and has potential to facilitate ultrasound small vessel imaging quantification.
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