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#32746350   2020/06/30 To Up

Towards a portable platform integrated with multi-spectral non-contact probes for delineating normal and breast cancer tissue based on near-infrared spectroscopy.

Currently, the confirmation of diagnosis of breast cancer is made by microscopic examination of an ultra-thin slice of a needle biopsy specimen. This slice is conventionally formalin-fixed and stained with hematoxylin-eosin and visually examined under a light microscope. This process is labor-intensive and requires highly skilled doctors (pathologists). In this paper, we report a novel tool based on near-infrared spectroscopy (Spectral-IRDx) which is a portable, non-contact, and cost-effective system and could provide a rapid and accurate diagnosis of cancer. The Spectral-IRDx tool performs absorption spectroscopy at near-infrared (NIR) wavelengths of 850 nm, 935 nm, and 1060 nm. We measure normalized detected voltage (Vdn) with the tool in 10 deparaffinized breast biopsy tissue samples, 5 of which were cancer (C) and 5 were normal (N) tissues. The difference in Vdn at 935 nm and 1060 nm between cancer and normal tissues is statistically significant with p-values of 0.0038 and 0.0022 respectively. Absorption contrast factor (N/C) of 1.303, 1.551, and 1.45 are observed for 850 nm, 935 nm, and 1060 nm respectively. The volume fraction contrast (N/C) of lipids and collagens are reported as 1.28 and 1.10 respectively. Higher absorption contrast factor (N/C) and volume fraction contrast (N/C) signifies higher concentration of lipids in normal tissues as compared to cancerous tissues, a basis for delineation. These preliminary results support the envisioned concept for non-invasive and non-carcinogenic NIR-based breast cancer diagnostic platform, which will be tested using a larger number of samples.
Uttam Mrinal Pal, Anil Vishnu Gk, Gayatri Gogoi, Saeed Rila, Saahil Shroff, Gokul Am, Manoj Varma, Vishnu Kurpad, Deb Baruah, Pronami Borah, Jayant S Vaidya, Hardik Jeetendra Pandya

2756 related Products with: Towards a portable platform integrated with multi-spectral non-contact probes for delineating normal and breast cancer tissue based on near-infrared spectroscopy.

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#32746211   2020/07/20 To Up

Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction.

Speed-of-sound has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. Speed-of-sound images can be reconstructed from time-of-flight measurements from ultrasound images acquired using conventional handheld ultrasound transducers. Variational Networks (VN) have recently been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction. Despite earlier promising results, these methods however do not generalize well from simulated to acquired data, due to the domain shift. In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access. This is made possible by incorporating simulations with varying complexity into training. We use loop unrolling of gradient descent with momentum, with an exponentially weighted loss of outputs at each unrolled iteration in order to regularize training. We learn norms as activation functions regularized to have smooth forms for robustness to input distribution variations. We evaluate reconstruction quality on ray-based and full-wave simulations as well as on tissue-mimicking phantom data, in comparison to a classical iterative (L-BFGS) optimization of this image reconstruction problem. We show that the proposed regularization techniques combined with multi-source domain training yield substantial improvements in the domain adaptation capabilities of VN, reducing median RMSE by 54% on a wave-based simulation dataset compared to the baseline VN. We also show that on data acquired from a tissue-mimicking breast phantom the proposed VN provides improved reconstruction in 12 milliseconds.
Melanie Bernhardt, Valery Vishnevskiy, Richard Rau, Orcun Goksel

1261 related Products with: Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction.

100μg100 ug 15 ml 100ug100ug170 tests100ug100μg

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#32746153   2020/07/31 To Up

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.

Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters, enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation.
Simon Graham, David Epstein, Nasir Rajpoot

2630 related Products with: Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.

10 ml100 ml250 mg100 ml10ml10ìg50 ml50 ml 1 G500 ml

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#32746152   2020/07/30 To Up

An enhanced visualization of DBT imaging using blind deconvolution and total variation minimization regularization.

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.
Ana M Mota, Matthew J Clarkson, Pedro Almeida, Nuno Matela

2859 related Products with: An enhanced visualization of DBT imaging using blind deconvolution and total variation minimization regularization.

1 kit(96 Wells)1000 tests100ug1 kit(96 Wells)25 mg2.5 mg2.5 mg100ul10 mg1 kit(96 Wells) 5 G

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#32746121   2020/06/24 To Up

Investigation of CdTe, GaAs, Se and Si as Sensor Materials for Mammography.

Despite the benefits of mammography investigations, some studies have shown that X-ray exposure from the mammography screening itself can statistically cause breast cancer in a small fraction of women. Therefore, a dose reduction in mammography is desirable. At the same time, there is a demand for a higher spatial resolution in mammographic imaging. The most promising way to achieve these goals is the use of advanced photon-processing semiconductor X-ray detectors with optimum sensor materials. This study addresses the investigation of the optimum semiconductor sensor material for mammography in combination with the photon-processing detector Medipix3RX. The influence of K-shell fluorescence from the sensor material on the achievable contrast-to-noise ratio is investigated, as well as the attenuation efficiency. The three different sensor materials, CdTe, GaAs, and Si are studied, showing advances of CdTe-sensors for mammography. Furthermore, a comparison of the contrast-to-noise ratio between a clinical Se-detector and Medipix3RX detectors with Si-and CdTe-sensors is shown using a self-produced mammography phantom that is based on real human tissue.
S Procz, G Roque, C Avila, J Racedo, R Rueda, I Santos, M Fiederle

1584 related Products with: Investigation of CdTe, GaAs, Se and Si as Sensor Materials for Mammography.

0.1ml (1mg/ml)100tests1ml100ml50 assays100ug Lyophilized100 tests1ml100ug Lyophilized

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#32746069   2020/07/22 To Up

Enhancing the X-ray differential phase contrast image quality with deep learning technique.

The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.
Yongshuai Ge, Peizhen Liu, Yifan Ni, Jianwei Chen, Jiecheng Yang, Ting Su, Huitao Zhang, Jinchuan Guo, Hairong Zheng, Zhi-Cheng Li, Dong Liang

1457 related Products with: Enhancing the X-ray differential phase contrast image quality with deep learning technique.

50 UG1 module100ug Lyophilized500 Units100.00 ug 500 Slides 1 kit1100ug Lyophilized

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#32745979   2020/07/22 To Up

Serum copper and zinc levels in breast cancer: A meta-analysis.

More and more studies have investigated the relationship between serum copper (Cu) and/or zinc (Zn) levels and breast cancer (BC). However, the results are inconsistent. It is unclear whether the serum Cu to Zn ratio (Cu/Zn) is associated with BC risk. Therefore, we evaluated serum Cu and Zn concentrations, and Cu/Zn in BC through meta-analysis.
Yue Feng, Jia-Wei Zeng, Qin Ma, Shuang Zhang, Jie Tang, Jia-Fu Feng

1643 related Products with: Serum copper and zinc levels in breast cancer: A meta-analysis.

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#32745978   2020/07/21 To Up

GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images.

Stain normalization of microscopic images is the first pre-processing step in any computer-assisted automated diagnostic tool. This paper proposes Geometry-inspired Chemical-invariant and Tissue Invariant Stain Normalization method, namely GCTI-SN, for microscopic medical images. The proposed GCTI-SN method corrects for illumination variation, stain chemical, and stain quantity variation in a unified framework by exploiting the underlying color vector space's geometry. While existing stain normalization methods have demonstrated their results on a single tissue and stain type, GCTI-SN is benchmarked on three cancer datasets of three cell/tissue types prepared with two different stain chemicals. GCTI-SN method is also benchmarked against the existing methods via quantitative and qualitative results, validating its robustness for stain chemical and cell/tissue type. Further, the utility and the efficacy of the proposed GCTI-SN stain normalization method is demonstrated diagnostically in the application of breast cancer detection via a CNN-based classifier.
Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, Devprakash Satpathy

2318 related Products with: GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images.

0.1ml (1mg/ml)0.1ml (1mg/ml)5mg

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#32745951   2020/07/27 To Up

Clinicopathological characteristics of gene-positive breast cancer in the United Arab Emirates.

Breast cancer is the most prevalent cancer in the United Arab Emirates (UAE). This is the first study to provide data on predisposition of breast cancer susceptibility genes with associated clinical and pathological aspects in the UAE.
Ajda Altinoz, Mouza Al Ameri, Warda Qureshi, Noura Boush, Satish Chandrasekhar Nair, Ahmed Abdel-Aziz

2045 related Products with: Clinicopathological characteristics of gene-positive breast cancer in the United Arab Emirates.

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