Recurrent residual U-Net for medical image segmentation
By Md. Zahangir Alom, C. Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari. Journal of Medical Imaging, 2019
731 citations recorded by the source corpus.
Abstract. Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net.
Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/c40a37fbf0402c0e98437c9d247dcc6a9b0fb662
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Citations in the corpus
- Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “skip connections [8], incorporating residual/dense convolution blocks [9], [10], attention mechanisms [11], [12], etc.”
- Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “First, we resized all images to a 224 × 224 pixels size, and then like [128], we used 1,815 images for training, 259 for validation, and 520 for the testing steps.”
- Deep semantic segmentation of natural and medical images: a review (2019, methodology): “75] applied U-Net to obtain initial segmentation probability maps and further improve them using LSTM for pancreas segmentation from 3D CT scans. Similarly, other works have also applied RNNs (LSTMs) [4,18,164,173,174] to medical image segmentation. 4.OptimizationFunctionbasedImprovements In additio”