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A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
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A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
Myocardial infarction (MI) is a scientific term that refers to heart attack. In this study, we combine induction of highly specific second harmonic generation (SHG) signals from non-centrosymmetric macromolecules such as fibrillar collagens together with two-photon excited cellular autofluorescence in infarcted mouse heart to quantitatively probe fibrosis, especially targeted at an early stage after MI. We present robust one-shot machine learning algorithms that enable determination of spatially resolved 2D structural organization of collagen as well as structural morphologies in heart tissues post-MI with spectral specificity and sensitivity. Detection, evaluation, and precise quantification of fibrosis extent at early stage would guide one to develop treatment therapies that may prevent further progression and determine heart transplant needs for patient survival.
Myocardial infarction (MI) is a scientific term that refers to heart attack. In this study, we combine induction of highly specific second harmonic generation (SHG) signals from non-centrosymmetric macromolecules such as fibrillar collagens together with two-photon excited cellular autofluorescence in infarcted mouse heart to quantitatively probe fibrosis, especially targeted at an early stage after MI. We present robust one-shot machine learning algorithms that enable determination of spatially resolved 2D structural organization of collagen as well as structural morphologies in heart tissues post-MI with spectral specificity and sensitivity. Detection, evaluation, and precise quantification of fibrosis extent at early stage would guide one to develop treatment therapies that may prevent further progression and determine heart transplant needs for patient survival.
