Neural architecture search for multi-tissue segmentation using convolutional and transformer-based networks in glioma segmentation

doi:10.11772/j.issn.1001-9081.2024070977

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (7): 2378-2386.DOI: 10.11772/j.issn.1001-9081.2024070977

• Multimedia computing and computer simulation • Previous Articles

Neural architecture search for multi-tissue segmentation using convolutional and transformer-based networks in glioma segmentation

Yongpeng TAO, Shiqi BAI(), Zhengwen ZHOU

School of Software，Dalian University of Foreign Languages，Dalian Liaoning 116044，China

Received:2024-07-09 Revised:2024-11-19 Accepted:2024-11-19 Online:2025-07-10 Published:2025-07-10
Contact: Shiqi BAI
About author:TAO Yongpeng， born in 1981， M. S.， associate professor. His research interests include medical image processing， medical big data processing.
BAI Shiqi， born in 2002， M. S. candidate. Her research interests include machine learning.
ZHOU Zhengwen， born in 1998， M. S. candidate. Her research interests include machine learning.
Supported by:
Scientific Research Fund of Liaoning Provincial Department of Education （General Project）(LJKZ1033)

基于卷积和Transformer神经网络架构搜索的脑胶质瘤多组织分割网络

陶永鹏, 柏诗淇(), 周正文

大连外国语大学软件学院，辽宁大连 116044

通讯作者: 柏诗淇
作者简介:陶永鹏（1981—），男，辽宁大连人，副教授，硕士，CCF会员，主要研究方向：医学图像处理、医疗大数据处理
柏诗淇（2002—），女，辽宁沈阳人，硕士研究生，主要研究方向：机器学习 dufl2021@163.com
周正文（1998—），女，重庆人，硕士研究生，主要研究方向：机器学习。
基金资助:
辽宁省教育厅科学研究经费资助项目（面上项目）(LJKZ1033)

Abstract

Abstract:

The significant variations in shape and size， blurred boundaries， and complex tissue structures of glioma in Magnetic Resonance Imaging （MRI） Images， lead to challenges in task for brain tumor segmentation. Typically， this task requires researchers with deep professional knowledge to design complex personalized network models， which is time-consuming and needs many human resources. To simplify the network design process and obtain the optimal network architecture automatically， a Neural Architecture Search for multi-tissue segmentation using Convolutional and Transformer-based Networks in glioma segmentation （NASCT-Net） was proposed to improve segmentation precision during construction of network architecture for multi-modal MRI brain tumor segmentation. Firstly， Neural Architecture Search （NAS） technology was applied to construction of an encoder， thereby forming stackable Neural Architecture Search ENcoder CNN （NAS-CNN） encoding modules to optimize the network structure for precise glioma segmentation automatically. Secondly， a feature encoding module based on Transformer was integrated at the lower layers of the encoder to improve the representation ability of relative positions and global information among tumor components. Finally， a Volume-Weighted Dice Loss function （VWDiceLoss） was constructed to address the imbalance between foreground and background. NASCT-Net was compared with methods such as Swin-Unet on BraTS2019 brain tumor datasets. Experimental results show that NASCT-Net has an average Dice Similarity Coefficient （DSC） improvement of 0.009 and an average Hausdorff Distance （HD） decrease of 1.831 mm， validating the effectiveness of NASCT-Net in enhancing the precision of multi-tissue brain tumor segmentation.

Key words: network architecture, Network Architecture Search (NAS), brain tumor segmentation, Convolutional Neural Network (CNN), Transformer

摘要：

脑胶质瘤在磁共振成像（MRI）图像中的形状大小变化大、边界模糊且组织结构复杂，这些特点导致了脑肿瘤分割任务的挑战性，通常这种任务需要具备深厚专业知识的研究人员设计复杂定制的网络模型才能完成。这一过程不仅耗时，而且需要大量的人力资源。为了简化网络设计流程并自动获取最优的网络结构，提出一种基于卷积和Transformer神经网络架构搜索的脑胶质瘤多组织分割网络（NASCT-Net），以在构建用于多模态MRI脑肿瘤分割的网络架构的过程中，提高分割的精确度。首先，将神经架构搜索（NAS）技术应用于编码器的构建，形成可堆叠的NAS编解码模块，以自动优化适用于脑胶质瘤精准分割的网络架构；其次，在编码器底层集成基于Transformer的特征编码模块，以增强对肿瘤各组之间的相对位置和全局信息的表征能力；最后，通过构建体积加权Dice损失函数（VWDiceLoss），解决前景与背景的不平衡问题。在BraTS2019脑肿瘤数据集上与Swin-Unet等方法进行比较的实验结果表明，NASCT-Net的平均Dice相似系数（DSC）提高了0.009，同时平均Hausdorff距离（HD）降低了1.831 mm，验证了NASCT-Net在提高脑肿瘤多组织分割精度方面的有效性。

关键词: 网络架构, 神经网络架构搜索, 脑肿瘤分割, 卷积神经网络, Transformer

CLC Number:

TP391.4

Yongpeng TAO, Shiqi BAI, Zhengwen ZHOU. Neural architecture search for multi-tissue segmentation using convolutional and transformer-based networks in glioma segmentation[J]. Journal of Computer Applications, 2025, 45(7): 2378-2386.

陶永鹏, 柏诗淇, 周正文. 基于卷积和Transformer神经网络架构搜索的脑胶质瘤多组织分割网络[J]. 《计算机应用》唯一官方网站, 2025, 45(7): 2378-2386.

Figures/Tables 15

Fig. 1 Architecture of NASCT-Net model

Fig. 2 Network structure of NASCT-Net

Tab. 1 Input and output dimensions of each module in NASCT-Net

网络结构	模块名称	输入特征尺寸	包含操作	输出特征尺寸
输入	输入层	$240 × 240 × 4$	Resize操作，Concat操作	$256 × 256 × 4$
输入	卷积层	$256 × 256 × 4$	$1 × 1$ 卷积操作	$256 × 256 × 16$
编码器	NAS-ENC模块 $× 3$	$256 × 256 × 16$	$下采样操作 N o r m a l 操作上采样操作 + 向下 R e s i z e 操作 × 3$	$32 × 32 × 256$
编码器	Transformer模块	$32 × 32 × 256$	$P . E . 操作 T r a n s f o r m e r × 3 F e a t u r e M a p p i n g + R e s i z e 操作$	$32 × 32 × 256$
解码器	跳跃连接层	$32 × 32 × 256$	Concat操作	$32 × 32 × 512$
解码器	NAS-DEC模块 $× 3$	$32 × 32 × 512$	$下采样操作 N o r m a l 操作上采样操作 + 向下 R e s i z e 操作 + C o n c a t × 3$	$256 × 256 × 16$
输出	输出层	$256 × 256 × 16$	$1 × 1$ 卷积操作+Resize操作	$256 × 256 × 3$

Tab. 1 Input and output dimensions of each module in NASCT-Net

网络结构	模块名称	输入特征尺寸	包含操作	输出特征尺寸
输入	输入层	$240 × 240 × 4$	Resize操作，Concat操作	$256 × 256 × 4$
输入	卷积层	$256 × 256 × 4$	$1 × 1$ 卷积操作	$256 × 256 × 16$
编码器	NAS-ENC模块 $× 3$	$256 × 256 × 16$	$下采样操作 N o r m a l 操作上采样操作 + 向下 R e s i z e 操作 × 3$	$32 × 32 × 256$
编码器	Transformer模块	$32 × 32 × 256$	$P . E . 操作 T r a n s f o r m e r × 3 F e a t u r e M a p p i n g + R e s i z e 操作$	$32 × 32 × 256$
解码器	跳跃连接层	$32 × 32 × 256$	Concat操作	$32 × 32 × 512$
解码器	NAS-DEC模块 $× 3$	$32 × 32 × 512$	$下采样操作 N o r m a l 操作上采样操作 + 向下 R e s i z e 操作 + C o n c a t × 3$	$256 × 256 × 16$
输出	输出层	$256 × 256 × 16$	$1 × 1$ 卷积操作+Resize操作	$256 × 256 × 3$

Fig. 3 Example structure of NAS-CNN module

Fig. 4 Update strategy for NAS-CNN modules

Fig. 5 Visual segmentation results of HGG using six different methods on BraTS2019 dataset

Tab. 2 Quantitative evaluation results of HGG segmentation of NASCT-Net and other methods on BraTS2019 dataset

方法	TC		ET		WT
方法	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓
3D U-Net^［41］	0.741	24.151	0.722	26.414	0.760	19.165
NAS-Unet^［36］	0.754	22.237	0.740	23.986	0.808	14.327
Attention U-Net^［25］	0.760	21.813	0.743	24.181	0.812	15.232
Swin-Unet^［31］	0.784	16.282	0.772	18.452	0.828	12.787
GCN^［27］	0.781	17.145	0.752	20.883	0.831	12.150
NASCT-Net	0.792	15.486	0.786	17.325	0.839	10.502

Fig. 6 Average brain tumor segmentation results using six different methods on BraTS2019 dataset

Fig. 7 Visual segmentation results of LGG using six different methods on BraTS2019 dataset

Tab. 3 Quantitative evaluation results of LGG segmentation of NASCT-Net and other methods on BraTS2019 dataset

方法	TC		ET		WT
方法	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓
3D U-Net^［41］	0.693	28.732	0.726	23.975	0.753	20.215
NAS-Unet^［36］	0.716	26.157	0.747	24.062	0.814	16.023
Attention U-Net^［25］	0.722	25.311	0.752	22.757	0.806	16.490
Swin-Unet^［31］	0.746	19.175	0.787	16.674	0.818	15.462
GCN^［27］	0.783	21.151	0.762	20.356	0.820	14.837
NASCT-Net	0.759	18.656	0.782	17.032	0.825	14.085

Fig. 8 Visual segmentation results of different methods on BraTS2021 dataset

Tab. 4 Quantitative evaluation results of brain tumor segmentation on BraTS2021 dataset

方法	TC		ET		WT
方法	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓	Dice↑	HD95/mm↓
3D U-Net^［41］	0.710	26.432	0.731	25.015	0.762	19.215
NAS-Unet^［36］	0.724	25.757	0.751	23.267	0.812	15.023
Attention U-Net^［25］	0.731	23.014	0.763	21.032	0.818	15.615
Swin-Unet^［31］	0.752	19.730	0.797	18.979	0.824	14.962
GCN^［27］	0.748	22.878	0.782	20.137	0.827	14.533
NASCT-Net	0.765	19.056	0.812	18.017	0.836	13.757

Fig. 9 Changes in Loss and Dice similarity coefficient during training and testing

Tab. 5 Quantitative evaluation results of ablation experiments

模型	CNN-Transformer结构	NAS	VWDice损失函数	Dice	HD95/mm	参数量/10⁶
Baseline	—	—	—	0.724	25.917	13.502
模型1	√	—	—	0.760	20.181	16.343
模型2	—	√	—	0.756	21.042	7.140
模型3	—	—	√	0.783	14.899	13.502
模型4	√	√	—	0.794	14.026	15.065
模型5	√	—	√	0.820	12.431	16.343
模型6	—	√	√	0.812	13.745	13.502
NASCT-Net	√	√	√	0.832	12.293	15.506

Fig. 10 NAS-CNN module structures obtained by proposed method

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Neural architecture search for multi-tissue segmentation using convolutional and transformer-based networks in glioma segmentation

基于卷积和Transformer神经网络架构搜索的脑胶质瘤多组织分割网络

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