Automatic and label-free detection of meningioma in dura mater using the combination of multiphoton microscopy and image analysis

Abstract

Significance: To prevent meningioma recurrence, it is necessary to detect and remove all corresponding tumors intraoperatively, including those in the adjacent dura mater.

Aim: Currently, the removal of meningiomas from the dura mater depends solely on cautious visual identification of lesions by a neurosurgeon. Inspired by the requirements for resection, we propose multiphoton microscopy (MPM) based on two-photon-excited fluorescence and second-harmonic generation as a histopathological diagnostic paradigm to assist neurosurgeons in achieving precise and complete resection.

Approach: Seven fresh normal human dura mater samples and 10 meningioma-infiltrated dura mater samples, collected from 10 patients with meningioma, were acquired for this study. First, multi-channel mode and lambda mode detection were utilized in the MPM to characterize the architectural and spectral features of normal and meningioma-infiltrated dura mater, respectively. Three imaging algorithms were then employed to quantify the architectural differences between the normal and meningioma-infiltrated dura mater through calculations of the collagen content, orientation, and alignment. Finally, MPM was combined with another custom-developed imaging algorithm to locate the meningioma within the dura mater and further delineate the tumor boundary.

Results: MPM not only detected meningioma cells in the dura mater but also revealed the morphological and spectral differences between normal and meningioma-infiltrated dura mater, providing quantitative information. Furthermore, combined with a self-developed image-processing algorithm, the precise borders of meningiomas in the dura mater could be accurately delineated.

Conclusions: MPM can automatically detect meningiomas in the dura mater label-free. With the development of advanced multiphoton endoscopy, MPM combined with image analysis can provide decision-making support for histopathological diagnosis, as well as offer neurosurgeons more precise intraoperative resection guidance for meningiomas.

Keywords: dura mater; meningioma; multiphoton microscopy; second-harmonic generation; two-photon excitation fluorescence.

Fig. 1

Schematics of MPM and identification steps of meningiomas in dura mater. (a) Flow of sample processing. (b) Architectural and spectral features of dura mater captured using MPM based on the multiphoton imaging and multiphoton spectrum. (c) Quantitative diagnostic information provided from MPM combined with the image processing algorithm. (d) Precise borders of meningiomas in dura mater rapidly delineated via MPM combined with the image processing algorithm. This can offer neurosurgeons more precise intraoperative resection guidance for meningiomas.

Fig. 2

Quantification analysis of collagen content in normal dura mater and meningioma-infiltrated dura mater. (a)–(d) Original magnified SHG image, image enhancement result, segmentation result of collagen position, and final segmentation result of collagen content in normal dura mater. (e)–(h) Original magnified SHG image, image enhancement result, segmentation result of collagen position, and final segmentation result of collagen content in the meningioma-infiltrated dura mater. Scale bar: $100\mu \mathrm{m}$.

Fig. 3

Representative collagen alignment results for normal dura mater and meningioma-infiltrated dura mater: (a), (e) original SHG images; (b), (f) fast Fourier transform power spectra; (c), (g) polar plots of collagen fiber orientation distributions (red line: mean orientation of fibers); (d), (h) collagen fiber alignments fitting semicircular von Mises distributions with parameter $k$. Scale bar: $20\mu \mathrm{m}$.

Fig. 4

Representative TPEF image (color-coded red), SHG image (color-coded green), overlaid SHG/TPEF image, and the corresponding H&E-stained images of the normal and meningioma-infiltrated dura mater. (a)–(d) TPEF image, SHG image, overlaid SHG/TPEF image, and corresponding H&E-stained image of normal dura mater; (e)–(h) TPEF image, SHG image, the overlaid SHG/TPEF image, and the corresponding H&E-stained image of meningioma-infiltrated dura mater. Scale bar: $100\mu \mathrm{m}$.

Fig. 5

Microstructural details and multiphoton spectra of normal and meningioma-infiltrated dura mater. (a)–(d) Magnified TPEF image, SHG image, overlaid TPEF/SHG image, and corresponding H&E-stained image of the chosen area (cyan-dotted box) in Fig. 4(c). (e)–(h) Magnified TPEF image, SHG image, overlaid TPEF/SHG image, and corresponding H&E-stained image of the chosen area (white-dotted box) in Fig. 4(c). (i)–(l) Magnified TPEF, SHG, and overlaid TPEF/SHG image and the corresponding H&E-stained image of the chosen area (white box) in Fig. 4(g). (m)–(p) Magnified TPEF image, SHG image, overlaid TPEF/SHG image, and the corresponding H&E-stained image of the chosen area (cyan box) in Fig. 4(g). (q) Normalized multiphoton emission spectrum of normal dura mater and meningioma-infiltrated dura mater. Fitting spectral emission of (r) normal dura mater and (s) meningioma-infiltrated dura mater. White arrows: collagen fibers emitting comparable SHG and TPEF signals; purple arrows: collagen fibers only emitting SHG signals; cyan arrows: blood vessel; white arrowheads: tumor cells; cyan arrowheads: collagen bundles. Scale bar: $20\mu \mathrm{m}$.

Fig. 6

Collagen orientation distribution in (a) normal dura mater and (b) meningioma-infiltrated dura mater.

Fig. 7

Identification of boundary of meningioma in dura mater. (a) Representative overlaid SHG/TPEF image; (b) result after converting to CIE Lab space; (c) result after converting to gray image and Gaussian filtering process; (d) rough boundary; (e) precise boundary; (f) final segment result. White line: the boundary automatically delineated by our proposed algorithm. Scale bar: $20\mu \mathrm{m}$.