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General General

Deep learning approach to improve tangential resolution in photoacoustic tomography.

In Biomedical optics express

In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image.

Rajendran Praveenbalaji, Pramanik Manojit

2020-Dec-01

General General

Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens.

In Biomedical optics express

Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.

Zhao Fang, Zhu Lanxin, Fang Chunyu, Yu Tingting, Zhu Dan, Fei Peng

2020-Dec-01

General General

Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography.

In Biomedical optics express

A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previously used for resolution enhancement of photography and optical microscopy images. We have adapted and improved this technique for OCT image generation. Conditional GANs (cGANs) were trained on a novel set of ultrahigh resolution spectral domain OCT volumes, termed micro-OCT, as the high-resolution ground truth (∼1 μm isotropic resolution). The ground truth was paired with a low-resolution image obtained by synthetically degrading resolution 4x in one of (1-D) or both axial and lateral axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo imaging of human labial (lip) tissue and mouse skin were used in separate feasibility experiments. Accuracy of resolution enhancement compared to ground truth was quantified with human perceptual accuracy tests performed by an OCT expert. The GAN loss in the optimization objective, noise injection in both the generator and discriminator models, and multi-scale discrimination were found to be important for achieving realistic speckle appearance in the generated OCT images. The utility of high-resolution speckle recovery was illustrated by an example of micro-OCT imaging of blood vessels in lip tissue. Qualitative examples applying the models to image data from outside of the training data distribution, namely human retina and mouse bladder, were also demonstrated, suggesting potential for cross-domain transferability. This preliminary study suggests that deep learning generative models trained on OCT images from high-performance prototype systems may have potential in enhancing lower resolution data from mainstream/commercial systems, thereby bringing cutting-edge technology to the masses at low cost.

Liang Kaicheng, Liu Xinyu, Chen Si, Xie Jun, Qing Lee Wei, Liu Linbo, Kuan Lee Hwee

2020-Dec-01

General General

Visualizing topical drug uptake with conventional fluorescence microscopy and deep learning.

In Biomedical optics express

Mapping the uptake of topical drugs and quantifying dermal pharmacokinetics (PK) presents numerous challenges. Though high resolution and high precision methods such as mass spectrometry offer the means to quantify drug concentration in tissue, these tools are complex and often expensive, limiting their use in routine experiments. For the many topical drugs that are naturally fluorescent, tracking fluorescence emission can be a means to gather critical PK parameters. However, skin autofluorescence can often overwhelm drug fluorescence signatures. Here we demonstrate the combination of standard epi-fluorescence imaging with deep learning for the visualization and quantification of fluorescent drugs in human skin. By training a U-Net convolutional neural network on a dataset of annotated images, drug uptake from both high "infinite" dose and daily clinical dose regimens can be measured and quantified. This approach has the potential to simplify routine topical product development in the laboratory.

Evans Conor L, Hermsmeier Maiko, Yamamoto Akira, Chan Kin F

2020-Dec-01

General General

Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.

In Biomedical optics express

In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on in vivo rat vasculature imaging further validated that noise-free, high resolution images can be obtained using this method.

Sharma Arunima, Pramanik Manojit

2020-Dec-01

General General

Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission.

In Surgical neurology international ; h5-index 27.0

Background : Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score.

Methods : We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0-3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared.

Results : The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900-1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively.

Conclusion : We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.

Katsuki Masahito, Kakizawa Yukinari, Nishikawa Akihiro, Yamamoto Yasunaga, Uchiyama Toshiya

2020

Artificial intelligence, Deep learning, Machine learning, Prediction model, Subarachnoid hemorrhage