Pancreatic Cystic Neoplasms Segmentation Network Combining Dual Decoding and Global Attention Upsampling Modules

doi:10.11938/cjmr20233073

Abstract

Abstract:

The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.

Key words: medical image segmentation, DGANet, deep learning, pancreas

CLC Number:

O482.53

Dai Junlong, He Cong, Wu Jie, Bian Yun. Pancreatic Cystic Neoplasms Segmentation Network Combining Dual Decoding and Global Attention Upsampling Modules[J]. Chinese Journal of Magnetic Resonance, 2024, 41(2): 151-161.

Figures/Tables 9

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胰腺囊性肿瘤	病例数/例	切片数/张	总数据/张
MCN	93	676	1349
SCN	125	673	1349

模型	数据来源	数据类型	Dice/%
VA-3DUNet^[21]	NIH	3D(CT)	86.19±9.03
Attention U-Net^[9]	CT-150	3D(CT)	84.00±8.70
Cascaded multitask 3-D fully convolutional networks^[11]	82(CT)	3D(CT)	85.9±5.10
HSSN^[12]	MRI	3D(MRI)	85.7±2.3
PanKNet^[13]	Pancreas-MRI	3D(MRI)	77.46±8.62
ConResNet^[22]	NIH	3D(CT)	86.06
PNet-MSA^[8]	MRI-79	2D(MRI)	80.7±7.40
Spatial aggregation of holistically-nested convolutional neural networks^[23]	NIH	2D(CT)	81.27±6.27
deep convolutional LSTM neural network^[24]	MRI-79	2D(MRI)	80.5±6.70
RSTN^[25]	NIH	2D(CT)	84.50±4.97
TransUnet^[26]	MICCAI 2015	2D(CT)	73.96
DGANet（本文模型）	长海医院	2D(MRI)	86.28±2.77