A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
This work addresses early detection of fibrosis in heart attack patients, which could guide treatment and transplant decisions, but it appears incremental as it applies existing one-shot learning to a specific medical imaging domain.
The study tackled the problem of early-stage fibrosis assessment in infarcted myocardium by developing a one-shot machine learning algorithm to analyze second harmonic generation images, achieving high spatial resolution and sensitivity in quantifying collagen assembly.
Myocardial infarction (MI) is a scientific term that refers to heart attack. In this study, we infer highly relevant second harmonic generation (SHG) cues from collagen fibers exhibiting highly non-centrosymmetric assembly 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 a robust one-shot machine learning algorithm that enables determination of 2D assembly of collagen with high spatial resolution along with its structural arrangement 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.