Improving Deep Segmentation of Abdominal Organs MRI by Post-Processing

Pedro Furtado

doi:10.3390/biomedinformatics1030007

Abstract

Today Deep Learning (DL) is state-of-the-art in medical imaging segmentation tasks, including accurate localization of abdominal organs in MRI images. But segmentation still exhibits inaccuracies, which may be due to texture similarities, proximity or confusion between organs, morphology variations, acquisition conditions or other parameters. Examples include regions classified as the wrong organ, some noisy regions and inaccuracies near borders. To improve robustness, the DL output can be supplemented by more traditional image postprocessing operations that enforce simple semantic invariants. In this paper we define and apply totally automatic post-processing operations applying semantic invariants to correct segmentation mistakes. Organs are assigned relative spatial location restrictions (atlas fencing), 3D organ continuity requirements (envelop continuity), and smoothness constraints. A reclassification is done within organ envelopes to correct classification mistakes, and noise is removed (fencing, enveloping, noise removal, re-classifying and smoothing). Our experimental evaluation quantifies the improvement and compares the resulting quality with prior work on DL-based organ segmentation. Based on the experiments, we conclude post-processing improved the Jaccard index over independent test MRI sequences by a sum of 12 to 25 percentage points over the four segmented organs. This work has an important impact on research and practical application of DL because it describes how to post-process, quantifies the advantages, and can be applied to any DL approach.

Highlights

Academic Editor: Jörn LötschReceived: 2 August 2021Accepted: 14 October 2021Published: 19 October 2021Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Magnetic Resonance Imaging (MRI) is an imaging technique based on capturing magnetic signal changes in the resonance of hydrogen protons after triggering radiofrequency pulses
The segmentation network has an encoder, which is a sequence of convolution stages that extract and compress features automatically from the original image, and a decoder, which is a sequence of deconvolution layers, and a final pixel classification layer
In the paragraphs we review previous deep learning approaches applied to segmentation of abdominal organs in both

Summary

Introduction

Magnetic Resonance Imaging (MRI) is an imaging technique based on capturing magnetic signal changes in the resonance of hydrogen protons after triggering radiofrequency pulses Computerized processing of those signals outputs MRI images which can be used for diagnosing medical conditions. Deep learning-based segmentation networks can learn to segment automatically either the 2D slices or the 3D volumes based on training examples. They are state-of-the-art in segmentation of this and other medical imaging contexts. The segmentation network has an encoder, which is a sequence of convolution stages (convolution layers together with regularization and pooling) that extract and compress features automatically from the original image, and a decoder, which is a sequence of deconvolution layers, and a final pixel classification layer.

Methods

Results

Conclusion