Two‐dimensional medical image segmentation based on U‐shaped structure

With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder‐decoder‐based U‐Net architecture and its variants. Most s...

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Vydané v:International journal of imaging systems and technology Ročník 34; číslo 1
Hlavní autori: Cai, Sijing, Xiao, Yuwei, Wang, Yanyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 01.01.2024
Wiley Subscription Services, Inc
Predmet:
Artificial neural networks
Coders
deep learning
Feature extraction
Image processing
Image resolution
Image segmentation
Lesions
Machine learning
medical image segmentation
Medical imaging
Modules
Spatial data
U‐net
ISSN:0899-9457, 1098-1098
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Shrnutí:With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder‐decoder‐based U‐Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU‐Net. In our proposed RAAU‐Net structure, which is a modified U‐shaped architecture, we aim to capture high‐level information while preserving spatial information and focusing on the regions of interest. RAAU‐Net comprises three main components: a feature encoder module that utilizes a pre‐trained ResNet‐18 model as a fixed feature extractor, a multi‐receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.23023