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Myocardial infarction (MI) is a scientific term that refers to heart attack.In this study, we infer highly relevant second harmonic generation (SHG) cuesfrom collagen fibers exhibiting highly non-centrosymmetric assembly togetherwith two-photon excited cellular autofluorescence in infarcted mouse heart toquantitatively probe fibrosis, especially targeted at an early stage after MI.We present a robust one-shot machine learning algorithm that enablesdetermination of 2D assembly of collagen with high spatial resolution alongwith its structural arrangement in heart tissues post-MI with spectralspecificity and sensitivity. Detection, evaluation, and precise quantificationof fibrosis extent at early stage would guide one to develop treatmenttherapies that may prevent further progression and determine heart transplantneeds for patient survival.

A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium