Real-time quantitative fluorescence imaging using a single snapshot optical properties technique for neurosurgical guidance

2015 ◽  
Author(s):  
Pablo A. Valdes ◽  
Joseph Angelo ◽  
Sylvain Gioux
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Fluorescence Imaging ◽  
Quantitative Fluorescence ◽  
Single Snapshot

scholarly journals Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning

2020 ◽  
Vol 11 (10) ◽  
pp. 5701 ◽  
Author(s):  
Enagnon Aguénounon ◽  
Jason T. Smith ◽  
Mahdi Al-Taher ◽  
Michele Diana ◽  
Xavier Intes ◽  
...  
Keyword(s):  
Optical Properties ◽  
Deep Learning ◽  
Real Time ◽  
Wide Field ◽  
High Quality ◽  
Profile Correction ◽  
Snapshot Imaging ◽  
Single Snapshot

scholarly journals Real-time, profile-corrected single snapshot imaging of optical properties

2015 ◽  
Vol 6 (10) ◽  
pp. 4051 ◽  
Author(s):  
Martijn van de Giessen ◽  
Joseph P. Angelo ◽  
Sylvain Gioux
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Time Profile ◽  
Snapshot Imaging ◽  
Single Snapshot

scholarly journals Real-time fluorescence imaging of anthocyanins complexed with diphenylboric acid 2-aminoethyl inside B16–F10 melanoma cells

2021 ◽  
Vol 189 ◽  
pp. 112849
Author(s):  
Madalina Nistor ◽  
Monica Focsan ◽  
Luiza Gaina ◽  
Mihai Cenariu ◽  
Adela Pintea ◽  
...  
Keyword(s):  
Real Time ◽  
Fluorescence Imaging ◽  
Melanoma Cells

scholarly journals Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks

2021 ◽  
Author(s):  
Biluo Shen ◽  
Zhe Zhang ◽  
Xiaojing Shi ◽  
Caiguang Cao ◽  
Zeyu Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Fluorescence Imaging ◽  
Convolutional Neural Networks ◽  
Frozen Section ◽  
Fluorescent Imaging ◽  
Pathological Diagnosis ◽  
Pathological Examination ◽  
Deep Convolutional Neural Networks ◽  
Fluorescence Images

Abstract Purpose Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon’s visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery. Methods Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image–guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000–1700 nm) fluorescence images were obtained. Deep convolutional neural networks (CNNs) combined with NIR-II fluorescence imaging (named as FL-CNN) were explored to automatically provide pathological diagnosis of glioma in situ in real-time during patient surgery. The pathological examination results were used as the gold standard. Results The developed FL-CNN achieved the area under the curve (AUC) of 0.945. Comparing to neurosurgeons’ judgment, with the same level of specificity >80%, FL-CNN achieved a much higher sensitivity (93.8% versus 82.0%, P < 0.001) with zero time overhead. Further experiments demonstrated that FL-CNN corrected >70% of the errors made by neurosurgeons. FL-CNN was also able to rapidly predict grade and Ki-67 level (AUC 0.810 and 0.625) of tumor specimens intraoperatively. Conclusion Our study demonstrates that deep CNNs are better at capturing important information from fluorescence images than surgeons’ evaluation during patient surgery. FL-CNN is highly promising to provide pathological diagnosis intraoperatively and assist neurosurgeons to obtain maximum resection safely. Trial registration ChiCTR ChiCTR2000029402. Registered 29 January 2020, retrospectively registered

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