Deep learning-based bias transfer for overcoming laboratory differences of microscopic images

TL;DR

This study evaluates and enhances generative models to correct laboratory-induced biases in microscopic images, significantly improving downstream segmentation and classification tasks in medical image analysis.

Contribution

It introduces improved generative model architectures tailored for bias correction in microscopy images, demonstrating enhanced performance over existing methods.

Findings

Best results achieved with U-Net cycleGANs and Fixed-Point GANs with additional losses

Bias adaptation improved segmentation of kidney glomeruli and podocytes

Classification accuracy for prostate biopsies increased by up to 14%

Abstract

The automated analysis of medical images is currently limited by technical and biological noise and bias. The same source tissue can be represented by vastly different images if the image acquisition or processing protocols vary. For an image analysis pipeline, it is crucial to compensate such biases to avoid misinterpretations. Here, we evaluate, compare, and improve existing generative model architectures to overcome domain shifts for immunofluorescence (IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine the performance of the generative models, the original and transformed images were segmented or classified by deep neural networks that were trained only on images of the target bias. In the scope of our analysis, U-Net cycleGANs trained with an additional identity and an MS-SSIM-based loss and Fixed-Point GANs trained with an additional structure loss led to the best results for the IF and H&E stained samples, respectively. Adapting the bias of the samples significantly improved the pixel-level segmentation for human kidney glomeruli and podocytes and improved the classification accuracy for human prostate biopsies by up to 14%.