一種迭代邊界優(yōu)化的醫學(xué)圖像小樣本分割網(wǎng)絡(luò )

賈熹濱; 郭雄; 王珞; 楊大為; 楊正漢

doi:10.16383/j.aas.c220994

一種迭代邊界優(yōu)化的醫學(xué)圖像小樣本分割網(wǎng)絡(luò )

doi: 10.16383/j.aas.c220994

賈熹濱^{1, 本,},
郭雄^1,,
王珞^1,,
楊大為^2,,
楊正漢^2,

1.
北京工業(yè)大學(xué)信息學(xué)部北京 100124
2.
首都醫科大學(xué)附屬北京友誼醫院北京 100050

基金項目: 國家重點(diǎn)研發(fā)項目中國和韓國政府間聯(lián)合研究項目(2019YFE0107800), 國家自然科學(xué)基金(62171298, 82071876, 62476015, 82372043, 82371904)資助

詳細信息

作者簡(jiǎn)介:
賈熹濱：北京工業(yè)大學(xué)信息學(xué)部教授. 主要研究方向為視覺(jué)信息認知與計算, 智能醫學(xué)圖像分析和診斷, 情感計算. 本文通信作者. E-mail: jiaxibin@bjut.edu.cn

郭雄：北京工業(yè)大學(xué)信息學(xué)部碩士研究生. 主要研究方向為計算機視覺(jué), 醫學(xué)圖像分割和小樣本學(xué)習. E-mail: guox@emails.bjut.edu.cn

王珞：北京工業(yè)大學(xué)信息學(xué)部講師. 主要研究方向為圖像檢索, 深度學(xué)習, 醫學(xué)影像處理和多模態(tài)數據融合. E-mail: wangluo@bjut.edu.cn

楊大為：首都醫科大學(xué)附屬北京友誼醫院副教授. 2020年獲得首都醫科大學(xué)博士學(xué)位. 主要研究方向為肝臟疾病影像診斷與研究. E-mail: yangdawei@ccmu.edu.cn

楊正漢：首都醫科大學(xué)附屬北京友誼醫院教授. 1999年獲得北京醫科大學(xué)博士學(xué)位. 主要研究方向為腹部疾病影像診斷, 肝細胞癌及癌前病變的早期影像診斷, MRI新技術(shù)的開(kāi)發(fā)與應用. E-mail: yangzhenghan@vip.163.com

計量
- 文章訪(fǎng)問(wèn)數: 557
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- 被引次數: 0
出版歷程
- 收稿日期: 2022-12-26
- 錄用日期: 2023-07-22
- 網(wǎng)絡(luò )出版日期: 2023-10-15
- 刊出日期: 2024-10-21

A Few-shot Medical Image Segmentation Network With Iterative Boundary Refinement

JIA Xi-Bin^{1, 本
,},
GUO Xiong^1
,,
WANG Luo^1
,,
YANG Da-Wei^2
,,
YANG Zheng-Han^2
,

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Friendship Hospital, Capital Medical University, Beijing 100050

Funds: Supported by National Key Research and Development Program of China and South Korea Intergovernmental Joint Research Project (2019YFE0107800) and National Natural Science Foundation of China ((62171298, 82071876, 62476015, 82372043, 82371904))

More Information

Author Bio:
JIA Xi-Bin　Professor at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers visual information cognition and computing, intelligent medical image analysis and diagnosis, and emotional calculation. Corresponding author of this paper

GUO Xiong　Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers computer vision, medical image segmentation, and few-shot learning

WANG Luo　Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers image retrieval, deep learning, medical image processing, and multi-modal data fusion

YANG Da-Wei　Associate professor at the Beijing Friendship Hospital, Capital Medical University. He received his Ph.D. degree from Capital Medical University in 2020. His main research interest is imaging diagnosis and research on liver disease

YANG Zheng-Han　Professor at the Beijing Friendship Hospital, Capital Medical University. He received his Ph.D. degree from Beijing Medical University in 1999. His research interest covers imaging diagnosis of abdominal diseases, early imaging diagnosis of hepatocellular carcinoma and precancerous lesions, and development and application of new MRI technology

摘要

摘要: 精準的醫學(xué)圖像自動(dòng)分割是臨床影像學(xué)診斷和影像三維重建的重要基礎. 但醫學(xué)圖像數據的目標對象間對比度差異小、受器官運動(dòng)影響大, 加之標注樣本規模小, 因此在小樣本下建立高性能的醫學(xué)分割模型仍是目前的難點(diǎn)問(wèn)題. 針對主流原型學(xué)習小樣本分割網(wǎng)絡(luò )對醫學(xué)圖像邊界分割性能差的問(wèn)題, 提出一種迭代邊界優(yōu)化的小樣本分割網(wǎng)絡(luò )(Iterative boundary refinement based few-shot segmentation network, IBR-FSS-Net). 以雙分支原型學(xué)習的小樣本分割框架為基礎, 引入類(lèi)別注意力機制和密集比較模塊(Dense comparison module, DCM), 對粗分割掩碼進(jìn)行迭代優(yōu)化, 引導分割模型在多次迭代學(xué)習過(guò)程中關(guān)注邊界, 從而提升邊界分割精度. 為進(jìn)一步克服醫學(xué)圖像訓練樣本少且多樣性不足問(wèn)題, 使用超像素方法生成偽標簽, 擴充訓練數據以提升模型泛化性. 在A(yíng)BD-MR和ABD-CT醫學(xué)圖像分割公共數據集上進(jìn)行實(shí)驗, 與現有多種先進(jìn)的醫學(xué)圖像小樣本分割方法進(jìn)行對比分析和消融實(shí)驗. 實(shí)驗結果表明, 該方法有效提升了未見(jiàn)醫學(xué)類(lèi)別的分割性能.
- 醫學(xué)圖像分割 /
- 小樣本學(xué)習 /
- 注意力機制 /
- 邊界優(yōu)化
Abstract: Accurate automatic segmentation of medical images is an important basis for clinical imaging diagnosis and 3D image reconstruction. However, medical image data has small contrast differences between target objects, is greatly affected by organ movement, and the scale of labeled samples is small. Therefore, it is still a difficult problem to establish a high-performance medical segmentation model under few samples. In view of the poor performance of the mainstream prototype learning few-shot segmentation network for medical image boundary segmentation, an iterative boundary refinement based few-shot segmentation network (IBR-FSS-Net) is proposed. Based on the few-shot segmentation framework of dual-branch prototype learning, the category attention mechanism and dense comparison module (DCM) are introduced to iteratively refine the coarse segmentation mask, and guide the segmentation model to focus on the boundary during multiple iterative learning processes, thereby improving the boundary segmentation accuracy. In order to further overcome the problem of few training samples and insufficient diversity of medical images, this paper uses the super-pixel method to generate pseudo-labels and expand the training data to improve the generalization of the model. Experiments on the mainstream ABD-MR and ABD-CT medical image segmentation public datasets are done, we conduct extensive comparative analysis and ablation experiments with various existing advanced medical image few-shot segmentation methods. The results show that our method effectively improves the segmentation performance of unseen medical categories.
- Medical image segmentation /
- few-shot learning /
- attention mechanism /
- boundary refinement
注釋:

1) 本文責任編委 XXX Recommended by Associate Editor BIAN Wei

HTML全文

圖 1 一種迭代邊界優(yōu)化的小樣本分割網(wǎng)絡(luò )

Fig. 1 An iterative boundary refinement based few-shot segmentation network

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圖 2 基于注意力機制的邊界優(yōu)化模塊

Fig. 2 Boundary refinement module based on attention mechanism

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圖 3 原型網(wǎng)絡(luò )結構圖

Fig. 3 Prototype network structure diagram

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圖 4 密集比較模塊

Fig. 4 Dense comparison module

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圖 5 基于超像素算法的樣本擴充方法

Fig. 5 Sample augmentation approach based on super-pixel algorithm

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圖 6 ABD-MR數據集劃分示意圖

Fig. 6 ABD-MR dataset partition diagram

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圖 7 醫學(xué)圖像小樣本分割網(wǎng)絡(luò )的Baseline模型

Fig. 7 Baseline model of few-shot medical image segmentation network

下載: 全尺寸圖片幻燈片

圖 8 核磁共振成像圖像中的4種器官樣例的預測分割掩碼

Fig. 8 Predicted segmentation masks for four organ samples in magnetic resonance images

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圖 9 電子計算機斷層掃描圖像中的4種器官樣例的預測分割掩碼

Fig. 9 Predicted segmentation masks for four organ samples in computed tomography images

下載: 全尺寸圖片幻燈片

表 1 ABD-CT和ABD-MR數據集上, 不同方法的Dice系數值 (%)

Table 1 Dice coefficient values with different models on ABD-CT and ABD-MR datasets (%)

方法	ABD-CT					ABD-MR
方法	脾臟	左腎	右腎	肝臟	平均值	脾臟	左腎	右腎	肝臟	平均值
SE-Net	0.23	32.83	14.34	0.27	11.91	51.80	62.11	61.32	27.43	50.66
PANet	25.59	32.34	17.37	38.42	29.42	50.90	53.45	38.64	42.26	46.33
SSL-ALPNet	60.25	63.34	54.82	73.65	63.02	67.02	73.63	78.39	73.05	73.02
GCN-DE	56.53	68.13	75.50	46.77	61.73	60.63	76.07	83.03	49.47	67.30
RP-Net	69.85	70.48	70.00	79.62	72.48	76.35	81.40	85.78	73.51	79.26
ADNet	—	—	—	—	—	75.92	75.28	83.28	80.81	78.82
PoissonSeg	52.33	50.11	47.02	58.74	52.05	52.85	50.58	53.57	61.03	54.51
AAS-DCL	66.36	64.71	69.95	71.61	68.16	74.86	76.90	83.75	69.94	76.36
IBR-FSS-Net	71.73	73.78	72.02	78.13	73.92	75.12	82.19	85.64	75.89	79.71

下載: 導出CSV

表 2 不同組件組合方式的Dice系數值 (%)

Table 2 Dice coefficient values with different component combinations (%)

組合方式	脾臟	左腎	右腎	肝臟	平均值
Baseline	62.33	63.65	66.87	64.18	64.26
Baseline+Concat	60.62	65.55	68.53	66.56	65.32
Baseline+BRM	66.63	72.10	74.83	69.17	70.68
Baseline+3Concat	61.20	68.72	70.38	66.95	66.81
Baseline+3BRM	75.12	82.19	85.64	75.89	79.71

下載: 導出CSV

表 3 不同邊界優(yōu)化模塊數量的Dice系數值 (%)

Table 3 Dice coefficient values with different number of boundary refinement modules (%)

組件	脾臟	左腎	右腎	肝臟	平均值
Baseline	62.33	63.65	66.87	64.18	64.26
Baseline+BRM	66.63	72.10	74.83	69.17	70.68
Baseline+2BRM	69.88	79.98	82.12	73.56	76.39
Baseline+3BRM	75.12	82.19	85.64	75.89	79.71
Baseline+4BRM	68.57	77.23	78.82	69.60	73.56
Baseline+5BRM	64.13	70.55	72.69	66.42	68.45

下載: 導出CSV

表 4 不同特征提取網(wǎng)絡(luò )的Dice系數值 (%)

Table 4 Dice coefficient values with different feature extraction networks (%)

骨干網(wǎng)絡(luò )	脾臟	左腎	右腎	肝臟	平均值
VGG-16	52.09	63.83	64.48	57.88	59.57
U-Net	69.66	78.94	80.46	72.15	75.30
Res U-Net	71.82	78.24	81.10	73.41	76.14
Attention U-Net	73.96	79.14	83.51	73.60	77.55
ResNet50	71.23	78.19	82.57	73.68	76.42
ResNet101	75.12	82.19	85.64	75.89	79.71

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表 5 不同度量網(wǎng)絡(luò )組合方式的Dice系數值 (%)

Table 5 Dice coefficient values with different combination of metric networks (%)

度量網(wǎng)絡(luò )組合方式	脾臟	左腎	右腎	肝臟	平均值
Prototypical-Net	70.92	80.61	83.70	74.48	77.43
DCM	71.53	81.36	83.44	74.81	77.79
DCM+Prototypical-Net	72.97	81.49	83.68	74.83	78.24
Prototypical-Net+DCM	75.12	82.19	85.64	75.89	79.71

下載: 導出CSV

表 6 ABD-CT和ABD-MR數據集上, 與其他少標注樣本下醫學(xué)圖像分割方法對比的Dice系數值 (%)

Table 6 Dice coefficient values with other medical segmentation models in case of less annotated sample on ABD-CT and ABD-MR datasets (%)

方法(比例)	ABD-CT					ABD-MR
方法(比例)	脾臟	左腎	右腎	肝臟	平均值	脾臟	左腎	右腎	肝臟	平均值
MagicNet (30%)	91.42	86.19	84.64	93.89	89.04	—	—	—	—	—
CVCL (部分)	95.40	94.60	94.60	96.70	95.33	—	—	—	—	—
C-CAM (0%)	—	—	—	—	—	74.16	81.00	84.75	72.68	78.15
IBR-FSS-Net (5%)	71.73	73.78	72.02	78.13	73.92	75.12	82.19	85.64	75.89	79.71

下載: 導出CSV

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