Optical Visualization of Cerebral Cortex by Label-Free Multiphoton Microscopy

Wang, Shu; Wang, Feng; Qiu, Lida; Ma, Yussanne; Lin, Ruolan; Chen, Yue; Chen, Fuxiang; Lin, Yuanxiang; Tu, Haohua; Wang, Xingfu; Chen, Jianxin

doi:10.1109/jstqe.2018.2854580

Cited by 10 publications

(14 citation statements)

References 33 publications

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“…With the increasing attention on MPM, many studies have demonstrated that MPM has great advantages in the feasibility of clinical translation [20,25,31,32]. However, compared to currently accepted clinical imaging systems (ultrasound, X-ray computed tomography or tomosynthesis, and MRI), MPM has limited penetration depth for highresolution imaging of tissues.…”

Section: Discussionmentioning

confidence: 99%

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Label-Free Identification of Early Stages of Breast Ductal Carcinoma via Multiphoton Microscopy

et al. 2020

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Breast cancer can be cured by early diagnosis. Appropriate and effective clinical treatment benefits from accurate pathological diagnosis. However, due to the lack of effective screening and diagnostic imaging methods, early stages of breast cancer often progress to malignant breast cancer. In this study, multiphoton microscopy (MPM) via two-photon excited fluorescence combined with second-harmonic generation was used for identifying the early stages of breast ductal carcinoma. The results showed differences in both cytological features and collagen distribution among normal breast tissue, atypical ductal hyperplasia, low-grade ductal carcinoma in situ, and high-grade ductal carcinoma in situ with microinvasion. Furthermore, three features extracted from the MPM images were used to describe differences in cytological features, collagen density, and basement membrane circumference in the early stages of breast ductal carcinoma. They revealed that MPM has the ability to identify early stages of breast ductal carcinoma label-free, which would contribute to the early diagnosis and treatment of breast cancer. This study may provide the groundwork for the further application of MPM in the clinic.

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Section: Discussionmentioning

confidence: 99%

“…MPM System. The MPM system used in this work has been described in detail before [20]. Briefly, a commercial laser scanning microscope (Zeiss LSM 880 META, Jena, Germany) equipped with an external automode-locked Ti:sapphire laser (140 fs, 80 MHz) was used for all MPM imaging.…”

Section: Methodsmentioning

confidence: 99%

Label-Free Identification of Early Stages of Breast Ductal Carcinoma via Multiphoton Microscopy

et al. 2020

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“…The average power was 5 to 10 mW on the sample. We have demonstrated that neurons and brain parenchyma produce obvious two‐photon excited fluorescence (TPEF) signal and weak second‐harmonic generation signal . Therefore, in this study, the GaAsP PMT array detectors were only used to detect TPEF signal form brain specimen, which covered the wavelength range of 430 to 695 nm (color‐coded red).…”

Section: Methodsmentioning

confidence: 99%

“…Campanella et al presented a multiple instance learning‐based deep learning system that used only the reported diagnoses as labels for training . In addition, deep learning has also shown potentials in TPM images, such as vasculature segmentation , the classification of ovarian tissue and focal cortical dysplasia . To our knowledge, deep learning techniques have not yet been applied to solve target detection problem at cellular scale on the field of label‐free TPM images.…”

Section: Introductionmentioning

confidence: 99%

Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

Wang

Lin

et al. 2019

Journal of Biophotonics

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Stroke is a significant cause of morbidity and long‐term disability globally. Detection of injured neuron is a prerequisite for defining the degree of focal ischemic brain injury, which can be used to guide further therapy. Here, we demonstrate the capability of two‐photon microscopy (TPM) to label‐freely identify injured neurons on unstained thin section and fresh tissue of rat cerebral ischemia‐reperfusion model, revealing definite diagnostic features compared with conventional staining images. Moreover, a deep learning model based on convolutional neural network is developed to automatically detect the location of injured neurons on TPM images. We then apply deep learning‐assisted TPM to evaluate the ischemic regions based on tissue edema, two‐photon excited fluorescence signal intensity, as well as neuronal injury, presenting a novel manner for identifying the infarct core, peri‐infarct area, and remote area. These results propose an automated and label‐free method that could provide supplementary information to augment the diagnostic accuracy, as well as hold the potential to be used as an intravital diagnostic tool for evaluating the effectiveness of drug interventions and predicting potential therapeutics.

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“…MPM system used in this study had been elaborated previously. 18,19 Briefly, the system was composed of a Zeiss LSM 880 system (Jena, Germany) and a Ti:sapphire femtosecond laser (140 fs, 80 MHz) tuned from 690 to 1064 nm (Chameleon Ultra, Coherent, Inc., Santa Clara, California). A 63×Plan-Apochromat oil immersion objective [numerical aperture (NA) = 1.4, Zeiss, Jena, Germany] and 10× Plan-Apochromat objective (NA ¼ 0.45, Zeiss, Jena, Germany) were used for focusing the excitation beam on the samples and collecting the TPEF and backscattered SHG signals.…”

Section: Multiphoton Microscopic Imaging Systemmentioning

confidence: 99%

Rapid, label‐free detection of intracranial germinoma using multiphoton microscopy

Fang

Wang

et al. 2019

Neurophoton.

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Accurate histopathological diagnosis is essential for facilitating the optimal surgical management of intracranial germinoma. Current intraoperative histological methods are time-and labor-intensive and often produce artifacts. Multiphoton microscopy (MPM) is a label-free imaging technique that can produce intraoperative histological images of fresh, unprocessed surgical specimens. We employ an MPM based on second-harmonic generation and two-photon excited fluorescence microscopy to image fresh, unfixed, and unstained human germinoma specimens. We show that label-free MPM is not only capable of identifying various cells in human germinoma tissue but also capable of revealing the characteristics of germinoma such as granuloma, stromal fibrosis, calcification, as well as the abnormal and uneven structures of blood vessels. In conjunction with custom-developed image-processing algorithms, MPM can further quantify and characterize the extent of stromal fibrosis and calcification. Our results provide insight into how MPM can deliver rapid diagnostic histological data that could inform the surgical management of intracranial germinoma.

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Optical Visualization of Cerebral Cortex by Label-Free Multiphoton Microscopy

Cited by 10 publications

References 33 publications

Label-Free Identification of Early Stages of Breast Ductal Carcinoma via Multiphoton Microscopy

Label-Free Identification of Early Stages of Breast Ductal Carcinoma via Multiphoton Microscopy

Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

Rapid, label‐free detection of intracranial germinoma using multiphoton microscopy

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