Deeply-supervised CNN for prostate segmentation

By Qikui Zhu, Bo Du, Baris Turkbey, Peter L. Choyke, Pingkun Yan. 2017

217 citations recorded by the source corpus.

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided intervention. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Medical Imaging

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Medical Imaging

Dinggang Shen

Citations in the corpus

Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “It has been shown that this architecture produces automatic segmentation results on medical images that is comparable to more complex architectures [16,21].”
UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation (2022, methodology): “CNN-based Segmentation Methods : Since the introduction of the U-Net design [28], several CNN-based approaches [2, 14, 37, 38] have extended the standard U-Net architecture for various medical image segmentation tasks.”
Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI (2017, background): “There are also some successful applications in medical image fields, such as cell detection [32, 33] and prostate segmentation [34, 35].”
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, methodology): “[50] used eight additional deeply supervised layers in their proposed architecture.”
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, background): “Previous works have demonstrated the excellent ability of deep supervision to improve the effectiveness of hidden layers [32], [53], [54]; thus, we introduce deeply supervised nets (DSNs) [31] into our network.”
UNETR: Transformers for 3D Medical Image Segmentation (2021, methodology): “CNN-based Segmentation Networks : Since the introduction of the seminal U-Net [36], CNN-based networks have achieved state-of-the-art results on various 2D and 3D various medical image segmentation tasks [15, 54, 49, 17, 28].”