Learning With Context Feedback Loop for Robust Medical Image Segmentation

TL;DR

This paper introduces a recurrent deep learning framework with a feedback loop for medical image segmentation, enhancing accuracy and robustness by iteratively refining predictions through high-level feature extraction.

Contribution

It proposes a novel recurrent framework combining encoder-decoder CNN and FCN-based feedback to improve segmentation quality and robustness in medical imaging.

Findings

Outperforms state-of-the-art methods on four clinical datasets.

Produces anatomically plausible segmentation results.

Effective on both single and multi-structure segmentation tasks.

Abstract

Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image. The predicted probabilistic output of the forward system is then encoded by a fully convolutional network (FCN)-based context feedback system. The encoded feature space of the FCN is then integrated back into the forward system's feed-forward learning process. Using the FCN-based context feedback loop allows the forward system to learn and extract more high-level image features and fix previous mistakes, thereby improving prediction accuracy over time. Experimental results, performed on four different clinical datasets, demonstrate our method's potential application for single and multi-structure medical image segmentation by outperforming the state of the art methods. With the feedback loop, deep learning methods can now produce results that are both anatomically plausible and robust to low contrast images. Therefore, formulating image segmentation as a recurrent framework of two interconnected networks via context feedback loop can be a potential method for robust and efficient medical image analysis.