Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Justin Lo,Steven Miller,Dafna Sussman,Roxana Azma,Mike Seed,Birgit Ertl-Wagner,Matthias W Wagner,Saiee Nithiyanantham,Abirami Kirubarajan,Sherwin Cho,Dylan Young,Jillian Cardinell

doi:10.3390/s21134490

Abstract

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep learning (DL) framework for 2D fetal MRI segmentation using a Cross Attention Squeeze Excitation Network (CASE-Net) for research and clinical applications. CASE-Net is an end-to-end segmentation architecture with relevant modules that are evidence based. The goal of CASE-Net is to emphasize localization of contextual information that is relevant in biomedical segmentation, by combining attention mechanisms with squeeze-and-excitation (SE) blocks. This is a retrospective study with 34 patients. Our experiments have shown that our proposed CASE-Net achieved the highest segmentation Dice score of 87.36%, outperforming other competitive segmentation architectures.

Highlights

magnetic resonance imaging (MRI) plays a key role in fetal diagnosis due to its high resolution, superb soft-tissue contrast, and 2D capabilities [1]
CASE-Net achieved a Dice Similarity Coefficient (DSC) of 87.36% which was significantly higher than the DSC of UNet, USE-Net, ATN-Net, and LinkNet by 2.19% (p = 0.048), 1.29% (p = 0.034), 5.09% (p = 0.019), and 4.64% (p = 0.021), respectively
With a 91.79% recall, CASE-Net outperformed the other architectures by 2.55% (p = 0.022), 1.29% (p = 0.059), 5.09% (p = 0.078), and 4.64% (p = 0.058)

Summary

Introduction

MRI plays a key role in fetal diagnosis due to its high resolution, superb soft-tissue contrast, and 2D capabilities [1]. DL employs convolutional neural networks (CNN) to perform an in-depth analysis of images and multidimensional data forms. These neural networks use a complex series of convolutions, maximum pooling, and up-sampling operations to learn advanced image analysis tasks efficiently and accurately. Previous studies have found that DL algorithms are robust and accurate They can reduce the time and cost required for segmentation and diagnostic tasks [3]. This makes DL an ideal tool for automated whole fetal segmentation, due to the diversity in both morphological and image quality in conventional fetal imaging

Methods

Results

Conclusion