Resection-inspired histopathological diagnosis of cerebral cavernous malformations using quantitative multiphoton microscopy

Abstract

Rationale: Cerebral cavernous malformation (CCM) is prone to recurring microhemorrhage, which can lead to drug-resistant epilepsy. Surgical resection is the first choice to control seizures for CCM-associated epilepsy. At present, removal of resection-related tissue only depends on cautious visual identification of CCM lesions and perilesional yellowish hemosiderin rim by the neurosurgeon. Inspired by the resection requirements, we proposed quantitative multiphoton microscopy (qMPM) for a histopathology-level diagnostic paradigm to assist clinicians in precisely complete resection. Methods: A total of 35 sections specimens collected from 12 patients with the CCM-related epilepsy were included in this study. First, qMPM utilized a label-free multi-channel selective detection to image the histopathological features based on the spectral characteristics of CCM tissues. Then, qMPM developed three customized algorithms to provide quantitative information, a vascular patterns classifier based on linear support vector machine, visualization of microhemorrhage regions based on hemosiderin-related parameters, and the CCM-oriented virtual staining generative adversarial network (CCM-stainGAN) was constructed to generate two types of virtual staining. Results: Focused on CCM lesion and perilesional regions, qMPM imaged malformed vascular patterns and micron-scale hemosiderin-related products. Four vascular patterns were automatically identified by the classifier with an area under the receiver operating characteristic curve of 0.97. Moreover, qMPM mapped different degrees of hemorrhage regions onto fresh tissue while providing three quantitative hemosiderin-related indicators. Besides, qMPM realized virtual staining by the CCM-stainGAN with 98.8% diagnostic accuracy of CCM histopathological features in blind analysis. Finally, we provided pathologists and surgeons with the qMPM-based CCM histopathological diagnostic guidelines for a more definitive intraoperative or postoperative diagnosis. Conclusions: qMPM can provide decision-making supports for histopathological diagnosis, and resection guidance of CCM from the perspectives of high-resolution precision detection and automated quantitative assessment. Our work will promote the development of MPM diagnostic instruments and enable more optical diagnostic applications for epilepsy.

A deep learning-based stripe self-correction method for stitched microscopic images

“…Fluorescence, SRS, and H&E are publicly available datasets. For the acquisition of MPM dataset, we used a multiphoton imaging system consisting of a commercial confocal microscope (Zeiss LSM 880 META, Jena, Germany) and an external mode-locked Ti: sapphire laser (140 fs, 80 MHz) 10 , 26 , in which the excitation wavelength was 810 nm and the average power of the samples was 30 mW. Tissues used for imaging were extracted during surgery and prepared as formalin-fixed and paraffin-embedded samples.…”

A deep learning-based stripe self-correction method for stitched microscopic images

“…Fluorescence and SRS are publicly available datasets. For the acquisition of MPM dataset, we used a multiphoton imaging system consisting of a commercial confocal microscope (Zeiss LSM 880 META, Jena, Germany) and an external mode-locked Ti: sapphire laser (140 fs, 80 MHz) 10,26 , in which the excitation wavelength was 810 nm and the average power of the samples was 30 mW. Tissues used for imaging were extracted during surgery and prepared as formalin-fixed and paraffin-embedded samples.…”

“…First of all, virtual pathological staining of fluorescent images has been proven to promote the clinical development of microscopic imaging techniques [25][26][27][28] . Therefore, we utilized a promising deeplearned virtual staining model, UTOM 27 , to transform non-uniform stripe MPM images of cerebral vascular malformations into H&E-stained images (Fig.…”