Integrating Image Quality Assessment Metrics for Enhanced Segmentation Performance in Reconstructed Imaging Datasets

Samiha Mirza, Apurva Gala, Pandu Devarakota, Pranav Mantini, Shishir K. Shah

Abstract: Addressing the challenge of ensuring high-quality data selection for segmentation models applied to reconstructed imaging datasets, particularly seismic and MRI data, is crucial for enhancing model performance. These datasets often suffer from quality variations due to the complex nature of their acquisition processes, leading to the model failing to generalize well on these datasets. This paper investigates the impact of incorporating Image Quality Assessment (IQA) metrics into the data selection process to mitigate this challenge. By systematically selecting images with the highest quality based on quantitative metrics, we aim to improve the training process of segmentation models. Our approach focuses on training salt segmentation models for seismic data and tumor segmentation models for MRI data, illustrating the influence of image quality on segmentation accuracy and overall model performance.