scholarly journals Computer-aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors using Label-free Multi-Photon Imaging

2021 ◽  
Author(s):  
Kana Kobayashi-Taguchi ◽  
Takashi Saitou ◽  
Yoshiaki Kamei ◽  
Akari Murakami ◽  
Kanako Nishiyama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Second Harmonic ◽  
Morphological Changes ◽  
Signal Strength ◽  
Semantic Segmentation ◽  
Area Ratio ◽  
Label Free ◽  
Tissue Sections ◽  
Computer Aided ◽  
Photon Imaging

Abstract Background Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors. They are pathologically classified as fibroepithelial tumors, composed of a proliferation of both epithelial and stroma. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Computer-aided diagnosis is playing a pivotal role in accurate and objective evaluation of medical images. This technology opens up a new route to a solution for diagnostic problems. Methods A combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. Quantitative signatures, the epithelial to stromal area ratio, and the collagen SHG signal strength were investigated for their ability to distinguish between FA and PT lesions. Results Multi-photon microscopy recordings of tissue sections revealed distinct morphology between the epithelia and stroma, and further indicated that stromal regions emit a strong SHG signal which derives from collagen fibrils. However, this signal strength differs between the lesions, suggesting differences of collagenous molecular composition between the two lesions. In order to investigate hypertrophy of the stroma and compare this to the epithelial areas, an image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network was performed. The deep learning-based analysis showed accurate separation of epithelial and stromal regions. Further investigation was conducted to determine if scoring the epithelial to stromal area ratio could be a marker for differentiating fibroadenoma and phyllodes tissues; we determined that most samples can be clearly separated but some are difficult to separate by the signature. Further investigations on the collagen SHG signal strength within the stromal area revealed accurate classification of the breast tissue lesions. Conclusions Molecular and morphological changes detected through the assistance of computational and label-free multi-photon imaging techniques enabled us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.

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 Mapping of real-time morphological changes in the neuronal cytoskeleton with label-free wide-field second-harmonic imaging: a case study of nocodazole

2019 ◽  
Vol 6 (04) ◽  
pp. 1
Author(s):  
Marie E. P. Didier ◽  
Carlos Macias-Romero ◽  
Claire Teulon ◽  
Pascal Jourdain ◽  
Sylvie Roke
Keyword(s):  
Real Time ◽  
Second Harmonic ◽  
Morphological Changes ◽  
Harmonic Imaging ◽  
Wide Field ◽  
Label Free ◽  
Neuronal Cytoskeleton

scholarly journals Objective assessment of stored blood quality by deep learning

2020 ◽  
Vol 117 (35) ◽  
pp. 21381-21390 ◽  
Author(s):  
Minh Doan ◽  
Joseph A. Sebastian ◽  
Juan C. Caicedo ◽  
Stefanie Siegert ◽  
Aline Roch ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Cytometry ◽  
Deep Learning ◽  
Morphological Changes ◽  
Objective Assessment ◽  
Feature Space ◽  
Label Free ◽  
Storage Duration ◽  
Imaging Flow Cytometry ◽  
The Impact

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

scholarly journals Deep-LUMEN Assay – Human lung epithelial spheroid classification from brightfield images using deep learning

2020 ◽  
Author(s):  
Lyan Abdul ◽  
Shravanthi Rajasekar ◽  
Dawn S.Y. Lin ◽  
Sibi Venkatasubramania Raja ◽  
Alexander Sotra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Morphological Changes ◽  
Drug Effects ◽  
Detection Algorithm ◽  
Extracellular Matrices ◽  
Label Free ◽  
Non Invasive ◽  
Lung Epithelial ◽  
Drug Treatments ◽  
Tissue Models

AbstractThree-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.Significance of the workDeep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.

scholarly journals Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue

2020 ◽  
Vol 22 (5) ◽  
pp. 1301-1309 ◽  
Author(s):  
Dan Li ◽  
Hui Hui ◽  
Yingqian Zhang ◽  
Wei Tong ◽  
Feng Tian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carotid Artery ◽  
Label Free ◽  
Bright Field ◽  
Generative Adversarial Network ◽  
Histological Image ◽  
Microscopic Images ◽  
Tissue Sections ◽  
Artery Tissue ◽  
Histological Staining

Abstract Purpose Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histological image analysis. Procedures In this study, we developed a deep learning-based method to transfer bright-field microscopic images of unlabeled tissue sections into equivalent bright-field images of histologically stained versions of the same samples. We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model. Results The results of a blind evaluation by board-certified pathologists illustrate that the virtual staining and standard histological staining images of rat carotid artery tissue sections and those involving different types of stains showed no major differences. Quantification of virtual and histological H&E staining in carotid artery tissue sections showed that the relative errors of intima thickness, intima area, and media area were lower than 1.6 %, 5.6 %, and 12.7 %, respectively. The training time of deep learning network was 12.857 h with 1800 training patches and 200 epoches. Conclusions This virtual staining method significantly mitigates the typically laborious and time-consuming histological staining procedures and could be augmented with other label-free microscopic imaging modalities.

scholarly journals Label-free assessment of red blood cell storage lesions by deep learning

2018 ◽  
Author(s):  
M. Doan ◽  
J. A. Sebastian ◽  
R. N. Pinto ◽  
C. McQuin ◽  
A. Goodman ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Morphological Changes ◽  
Dark Field ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Life Saving ◽  
Laboratory Procedures ◽  
Stored Blood

AbstractBlood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.Stored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.We demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.Deep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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