Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation

Li, Song; Tso, Kwok Fai; He, Kaijian

doi:10.1016/j.eswa.2019.113131

Cited by 68 publications

(32 citation statements)

References 38 publications

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“…Tables VI and VII show the comparison between our method and state‐of‐the‐art methods on the LiTS dataset. The methods based on encoder–decoder structure usually achieve better results in liver segmentation; for instance, Li et al 33 receive 0.960 DG and 0.962 DC for liver segmentation. Bellver et al 11 use a cascaded FCN structure and a detection network to segment liver tumors.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images

Li¹,

Liu

Ma³

et al. 2020

Medical Physics

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Purpose The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision‐making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time‐consuming and subjective. Computer‐aided segmentation methods have been widely used in the segmentation of liver and liver tumors. However, due to the diversity of shape, volume, and image intensity, the segmentation is still a difficult task. In this study, we present a Spatial Feature Fusion Convolutional Network (SFF‐Net) to automatically segment liver and liver tumors from CT images. Methods First, we extract side‐outputs at each convolutional block in SFF‐Net to make full use of multiscale features. Second, skip‐connections are added in the down‐sampling phase, therefore, the spatial information can be efficiently transferred to later layers. Third, we present feature fusion blocks (FFBs) to merge spatial features and high‐level semantic features from early layers and later layers, respectively. Finally, a fully connected 3D conditional random fields (CRFs) is applied to refine the liver and liver tumor segmentation results. Results We test our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge dataset. The Dice Global (DG) score, Dice per case (DC) score, Volume Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), and tumor precision score are calculated to evaluate the liver and liver tumor segmentation accuracies. For the liver segmentation, DG is 0.955; DC is 0.937; VOE is 0.106; and ASSD is 3.678. For the tumor segmentation, DG is 0.746; DC is 0.592; VOE is 0.416; ASSD is 1.585 and the tumor precision score is 0.369. Conclusions The SFF‐Net learns more spatial information by adding skip‐connections and feature fusion blocks. The experiments validate that our method can accurately segment liver and liver tumors from CT images.

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Section: Experiments and Resultsmentioning

confidence: 99%

Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images

Li¹,

Liu

Ma³

et al. 2020

Medical Physics

View full text Add to dashboard Cite

show abstract

“…Some methods are based on 2D networks or 3D networks respectively. [33] proposed a bottleneck feature supervised (BS) 2D U-Net which uses convolution kernels of different sizes to obtain multi-scale feature maps for live and tumor segmentation. [34] proposed GIU-Net that combines an improved 2D U-Net neural network model with graph cutting for liver segmentation.…”

Section: Liver and Tumor Segmentationmentioning

confidence: 99%

MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation

et al. 2020

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Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation. Unlike the previous works which extract the context information of medical image via applying the multi-scale feature fusion, we propose a novel network named Multi-scale Attention Net (MA-Net) by introducing self-attention mechanism into our method to adaptively integrate local features with their global dependencies. The MA-Net can capture rich contextual dependencies based on the attention mechanism. We design two blocks: Position-wise Attention Block (PAB) and Multi-scale Fusion Attention Block (MFAB). The PAB is used to model the feature interdependencies in spatial dimensions, which capture the spatial dependencies between pixels in a global view. In addition, the MFAB is to capture the channel dependencies between any feature map by multi-scale semantic feature fusion. We evaluate our method on the dataset of MICCAI 2017 LiTS Challenge. The proposed method achieves better performance than other state-of-the-art methods. The Dice values of liver and tumors segmentation are 0.960±0.03 and 0.749±0.08 respectively.

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“…Also, Wang et al [31] integrate the inception module in U-Net architecture for segmentation of left atrial. Li and Tso [32] in cooperated inception modules and dilated inception modules in U-Net architecture for liver and tumor segmentation. Furthermore, Zang Z.et al [33] integrates the inception module with a dense connection into U-Net architecture.…”

Section: Related Workmentioning

confidence: 99%