Skip to main content
Top
Published in: International Journal of Computer Assisted Radiology and Surgery 3/2024

20-11-2023 | Original Article

Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection

Authors: Takaaki Sugino, Taichi Kin, Nobuhito Saito, Yoshikazu Nakajima

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 3/2024

Login to get access

Abstract

Purpose

Accurate and automatic segmentation of basal ganglia from magnetic resonance (MR) images is important for diagnosis and treatment of various brain disorders. However, the basal ganglia segmentation is a challenging task because of the class imbalance and the unclear boundaries among basal ganglia anatomical structures. Thus, we aim to present an encoder–decoder convolutional neural network (CNN)-based method for improved segmentation of basal ganglia by focusing on skip connections that determine the segmentation performance of encoder–decoder CNNs. We also aim to reveal the effect of skip connections on the segmentation of basal ganglia with unclear boundaries.

Methods

We used the encoder–decoder CNNs with the following five patterns of skip connections: without skip connection, with full-resolution horizontal skip connection, with horizontal skip connections, with vertical skip connections, and with crossover-typed skip connections (the proposed method). We compared and evaluated the performance of the CNNs in the experiment of basal ganglia segmentation using T1-weighted MR brain images of 79 patients.

Results

The experimental results showed that the skip connections at each scale level help CNNs to acquire multi-scale image features, the vertical skip connections contribute on acquiring finer image features for segmentation of smaller anatomical structures with more blurred boundaries, and the crossover-typed skip connections, a combination of horizontal and vertical skip connections, provided better segmentation accuracy.

Conclusion

This paper investigated the effect of skip connections on the basal ganglia segmentation and revealed the crossover-typed skip connections might be effective for improving the segmentation of basal ganglia with the class imbalance and the unclear boundaries.
Literature
16.
26.
31.
go back to reference Nolden M, Zelzer S, Seitel A, Wald D, Müllar M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction Toolkit: challenges and advances: 10years of open-source development. Int J Comput Assist Radiol Surg 8:607–620. https://doi.org/10.1007/s11548-013-0840-8CrossRefPubMed Nolden M, Zelzer S, Seitel A, Wald D, Müllar M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction Toolkit: challenges and advances: 10years of open-source development. Int J Comput Assist Radiol Surg 8:607–620. https://​doi.​org/​10.​1007/​s11548-013-0840-8CrossRefPubMed
Metadata
Title
Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection
Authors
Takaaki Sugino
Taichi Kin
Nobuhito Saito
Yoshikazu Nakajima
Publication date
20-11-2023
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 3/2024
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-023-03015-9

Other articles of this Issue 3/2024

International Journal of Computer Assisted Radiology and Surgery 3/2024 Go to the issue