scholarly journals Automatic cell counting from stimulated Raman imaging using deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254586
Author(s):  
Qianqian Zhang ◽  
Kyung Keun Yun ◽  
Hao Wang ◽  
Sang Won Yoon ◽  
Fake Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stimulated Raman Scattering ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Cell Counting ◽  
Label Free ◽  
Characteristic Analysis ◽  
Human Brain Tumor ◽  
Training Samples ◽  
Stimulated Raman

In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting from label-free SRS images has been challenging due to the limited contrast of cells and tissue, along with the heterogeneity of tissue morphology and biochemical compositions. To this end, a deep learning-based cell counting scheme is proposed by modifying and applying U-Net, an effective medical image semantic segmentation model that uses a small number of training samples. The distance transform and watershed segmentation algorithms are also implemented to yield the cell instance segmentation and cell counting results. By performing cell counting on SRS images of real human brain tumor specimens, promising cell counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in terms of cell counting correlation between SRS and histological images with hematoxylin and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and potential of performing cell counting automatically in near real time and encourages the study of applying deep learning techniques in biomedical and pathological image analyses.

scholarly journals Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

2021 ◽  
Author(s):  
Zhijie Liu ◽  
Wei Su ◽  
Jianpeng Ao ◽  
Min Wang ◽  
Qiuli Jiang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Stimulated Raman Scattering ◽  
Semantic Segmentation ◽  
Resection Margins ◽  
Intratumor Heterogeneity ◽  
Maximum Speed ◽  
Histopathological Diagnosis ◽  
Single Shot ◽  
Label Free ◽  
Stimulated Raman

Abstract Gastroscopic biopsy provides the only effective way for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrated single-shot femtosecond SRS (femto-SRS) could reach the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy was imaged in < 60 seconds, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) was constructed based on images from 279 patients that predicts gastric cancer with accuracy > 96%. We further demonstrated semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

scholarly journals Utilizing Stimulated Raman Scattering Microscopy To Study Intracellular Distribution of Label-Free Ponatinib in Live Cells

2019 ◽  
Vol 63 (5) ◽  
pp. 2028-2034 ◽  
Author(s):  
Kristel Sepp ◽  
Martin Lee ◽  
Marie T. J. Bluntzer ◽  
G. Vignir Helgason ◽  
Alison N. Hulme ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Stimulated Raman Scattering ◽  
Intracellular Distribution ◽  
Live Cells ◽  
Label Free ◽  
Stimulated Raman

scholarly journals Blood Cell Count Using Deep Learning Semantic Segmentation

2019 ◽  
Author(s):  
Thanh Tran ◽  
Lam Binh Minh ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Cell Count ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Semantic Segmentation ◽  
Peripheral Blood Smear ◽  
Cell Counting ◽  
Blood Cell Count

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

scholarly journals Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering

2019 ◽  
Vol 116 (32) ◽  
pp. 15842-15848 ◽  
Author(s):  
Yuta Suzuki ◽  
Koya Kobayashi ◽  
Yoshifumi Wakisaka ◽  
Dinghuan Deng ◽  
Shunji Tanaka ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Scattering ◽  
Cancer Biology ◽  
High Speed ◽  
Stimulated Raman Scattering ◽  
Chemical Imaging ◽  
Fluorescent Labeling ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Stimulated Raman

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

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