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.