Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation.

Jiao-Song Long,Guang-Zhi Ma,Ren-Chao Jin,En-Min Song

doi:10.3390/s21093232

Jiao-Song Long, Guang-Zhi Ma + Show 2 more

Open Access

https://doi.org/10.3390/s21093232

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Abstract

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.

Highlights

As pooling is easy to lose the useful spatial information when the feature map resolution is reduced, we propose the multi-branch pooling (MP) by using multiple max pooling with different kernel sizes in parallel to reduce the information missing and collect the neighborhood information with a suitable receptive field; In the decoding part, we propose the multi-branch dense prediction, an information extractor, called MDP, to capture multi-scale channel information for the information compensating
We performed an ablation study to investigate the efficacy of employing multi-branch pooling (MP), multi-branch dense prediction (MDP), and multi-branch output module by using five-fold cross-validation
Our analysis shows that the MP and MDP with multi-branch output are effective in segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), if the combination of different kernel sizes in MP and different groups in MDP are selected by a manual setting, which may be tedious and prone to errors if applied in some extreme cases

Summary

Introduction

The segmentation of brain tissues from magnetic resonance (MR) images is of primary importance for subsequent diagnosis, pathological analysis, prognosis assessment, and brain development monitoring [1]. MR images have different kinds of modalities, including. T1, T1C, T2, PD, T1IR, and FLAIR, and each reflects particular characteristics of tissue regions in brain. Both T2 and FLAIR sequences describe low signals in the white matter region and high signals in the gray matter region. T2 depicts marked high signals for the cerebrospinal fluid, where FLAIR shows low or no intensity signals [2,3]. We can aggregate these multiple modalities to capture richer information to improve brain tissue segmentation performance

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