Abstract: Objective and Impact Statement. There is a need to develop high-performance and low-cost data augmentation strategies for intelligent skin cancer screening devices that can be deployed in rural or underdeveloped communities. The proposed strategy can not only improve the classification performance of skin lesions but also highlight the potential regions of interest for clinicians' attention. This strategy can also be implemented in a broad range of clinical disciplines for early screening and automatic diagnosis of many other diseases in low resource settings. Methods. We propose a high-performance data augmentation strategy of search space 10(1), which can be combined with any model through a plug-and-play mode and search for the best argumentation method for a medical database

Abstract: PURPOSE: The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. METHODS: 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0-C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were

Abstract: Traditional line-scan Raman imaging features a rapid imaging speed while preserving complete spectral information, yet has diffraction-limited resolution. Sinusoidally structured line excitation can yield an improvement in the lateral resolution of the Raman image along the line's direction. However, given the need for the line and spectrometer slit to be aligned, the resolution in the perpendicular direction remains diffraction limited. To overcome this, we present here a galvo-modulated structured line imaging system, where a system of three galvos can arbitrarily orient the structured line on the sample plane, while keeping the beam aligned to the spectrometer slit in the detection plane. Thus, a two-fold isotropic improvement in the lateral resolution fold is possible. We dem

Abstract: In quest of an efficient delivery system and for accurate delivery of drug to the ailment site, various researchers focused on development of a versatile delivery system. In progression, investigations on novel materials gained spotlight to add new dimension in the delivery devices to provide a more compatible and innovative platform for drug delivery. For innovative delivery systems, biomaterials like nanocelluloses were exploited for designing various scaffolds and delivery devices like microneedles. Microneedle devices are current trends of drug delivery where nanocellulose has evolved as protagonist for delivery aspects andstrengthen the structural integrity of device. Nanocelluloses provided a wide choice for polymers to design biodegradable microneedle device. Nanocellulose

Abstract: BACKGROUND: Near-infrared (NIR) autofluorescence detection is an effective method for identifying parathyroid glands (PGs) in thyroidectomy or parathyroidectomy. Fiber optical probes provide quantitative autofluorescence measurements for PG detection owing to its high sensitivity and high excitation light cut-off efficiency at a fixed detection distance. However, an optical fiber probe lacks the imaging capability and cannot map the autofluorescence distribution on top of normal tissue background. Therefore, there is a need for intraoperative mapping of PGs with high sensitivity and imaging resolution. METHODS: We have developed a fluorescence scanning and projection (FSP) system that combines a scanning probe and a co-axial projector for intraoperative localization and in situ d

Abstract: Though widely used, most deep convolutional neural networks fail to capture prediction uncertainty, which can be crucial in scenarios such as automotive applications and disease diagnosis. Aleatoric and epistemic uncertainties have been proposed in the Bayesian deep learning framework for regression and classification, which require training images with unambiguous labels for success. Some situations do not have precise labels by nature, such as age estimation or lesion contour annotation by different physicians in the real world. Label distribution learning (LDL) has been proposed to account for the label ambiguity. However, uncertainty estimation has not been studied for LDL. This study presents a Bayesian deep label distribution learning (BLDL) to obtain the uncertainties of L

Zhang R, Yang R, Lang Z, et al. Coaxial projective imaging for sentinel lymph node mapping in melanoma. JAAD Case Rep. Sep 2021;15:46-50. doi:10.1016/j.jdcr.2021.06.023

Abstract: Soft tissue sarcoma (STS) is a locally aggressive and infiltrative tumour in dogs. Surgical resection is the treatment of choice for local tumour control. Currently, post-operative pathology is performed for surgical margin assessment. Spectral-domain optical coherence tomography (OCT) has recently been evaluated for its value for surgical margin assessment in some tumour types in dogs. The purpose of this study was to develop an automatic diagnosis system that can assist clinicians in real-time for OCT image interpretation of tissues at surgical margins. We utilized a ResNet-50 network to classify healthy and cancerous tissues. A patch-based approach was adopted to achieve accurate classification with limited training data (80 cancer images, 80 normal images) and the validation

Abstract: BACKGROUND: The naked-eye invisibility of indocyanine green fluorescence limits the application of near-infrared fluorescence imaging (NIR) systems for real-time navigation during sentinel lymph node biopsy (SLNB) in patients with breast cancer undergoing surgery. This study aims to evaluate the effectiveness and safety of a novel NIR system in visualizing indocyanine green fluorescence images in the surgical field and the application value of combined methylene blue (MB) and the novel NIR system in SLNB. METHODS: Sixty patients with clinical node-negative breast cancer received indocyanine green (ICG) and MB as tracers. Two NIR system instruments, namely, lymphatic fluorescence imaging system (LFIS) designed by the University of Science and Technology of China and vascular image

Abstract: Collagen alignment has shown clinical significance in a variety of diseases. For instance, vulvar lichen sclerosus (VLS) is characterized by homogenization of collagen fibers with increasing risk of malignant transformation. To date, a variety of imaging techniques have been developed to visualize collagen fibers. However, few works focused on quantifying the alignment quality of collagen fiber. To assess the level of disorder of local fiber orientation, the homogeneity index (HI) based on limiting entropy is proposed as an indicator of disorder. Our proposed methods are validated by verification experiments on Poly Lactic Acid (PLA) filament phantoms with controlled alignment quality of fibers. A case study on 20 VLS tissue biopsies and 14 normal tissue biopsies shows that HI ca

Abstract: This paper reports a new type of augmented reality (AR) system that integrates a Microsoft HoloLens device with a three-dimensional (3D) point tracking module for medical training and telementored surgery. In this system, a stereo camera is used to track the 3D position of a scalpel and transfer its coordinates wirelessly to a HoloLens device. In the scenario of surgical training, a virtual surgical scene with pre-recorded surgical annotations is superimposed with the actual surgical scene so that the surgical trainee is able to operate following virtual instructions. In the scenario of telementored surgery, the virtual surgical scene is co-registered with the actual surgical scene so that the virtual scalpel remotely mentored by an experienced surgeon provides the AR guidance fo

Abstract: Retinal diseases and systemic diseases, such as diabetic retinopathy (DR) and Alzheimer’s disease, may manifest themselves in the retina, changing the retinal oxygen saturation (SO2) level or the retinal vascular structures. Recent studies explored the correlation of diseases with either retina vascular structures or SO2 level, but not both due to the lack of proper instrument or methodology. In this study, we applied a dual-modal fundus camera and developed a deep learning-based analysis method to simultaneously acquire and quantify the SO2 and vascular structures. Deep learning was used to automatically locate the optic discs and segment arterioles and venules of the blood vessels. We then sought to apply machine learning methods, such as random forest (RF) and support vector m