Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

Sun, Changjian; Guo, Shuxu; Zhang, Huimao; Li, Jing; Chen, Meimei; Ma, Shengli; Jin, Lanyi; Liu, Xiaoming; Li, Xueyan; Qian, Xiaohua

doi:10.1016/j.artmed.2017.03.008

Cited by 191 publications

(87 citation statements)

References 20 publications

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“…Many researchers advance this stream using deep learning methods in the liver and tumor segmentation problem and the literature can be classified into two categories broadly. (1) 2D FCNs, such arXiv:1709.07330v3 [cs.CV] 3 Jul 2018 as UNet architecture [15], the multi-channel FCN [16], and the FCN based on VGG-16 [17]. (2) 3D FCNs, where 2D convolutions are replaced by 3D convolutions with volumetric data input [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that most liver tumor segmentation methods[16, 19, 52- 55] utilized additional datasets for training and tested on the 3DIRCADb dataset. For example, Li et al[52], Sun et al[16] and Lu et al[19] collected additional clinical data from hospitals as the training set. Moghbel et al[53] utilized additional the MIDAS dataset while Li et al[54] used the SLIVER07 dataset in the training, respectively.…”

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confidence: 99%

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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Chen

et al. 2018

IEEE Trans. Med. Imaging

1,962

974

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Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Chen

et al. 2018

IEEE Trans. Med. Imaging

1,962

974

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“…Sun et al [16] used a segmentation CNN conceptually similar to the FCN architecture of [6], where the AlexNet [17] CNN was used as the encoder. On the 3DIRCADb dataset, Sun et al reported the VOE of 15.6 ± 4.3%.…”

Section: Related Fully-automatic Methodsmentioning

confidence: 99%

Automatic segmentation of kidney and liver tumors in CT images

Efremova¹,

Коновалов²,

Siriapisith³

et al. 2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

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Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MIC-CAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores.

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“…The segmentation algorithms for liver and liver tumors were mainly divided into four categories: regional growth, graph cut, level set, and deep learning . The segmentation algorithm in this paper was based on deep learning, so we mainly reviewed several classic liver and liver tumor segmentation algorithms based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…A two‐channel convolution was added to predict the probability of lesions or the liver at each output location, and then the output is up‐sampled to the original pixel for end‐to‐end learning using a deconvolution layer. Sun et al designed a multichannel FCN to segment liver tumors from enhanced CT images. Because each stage of the enhanced CT data provided unique information about the pathological features, the method trained a network for each stage and then fused their high‐level features.…”

Section: Introductionmentioning

confidence: 99%

Liver tumor segmentation based on 3D convolutional neural network with dual scale

Liu

Tian

2019

J Applied Clin Med Phys

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Purpose: Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location.Methods: To solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three-dimensional dual path multiscale convolutional neural network (TDP-CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy.Results: In the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second. Conclusions:The experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation. K E Y W O R D S 3D convolution, convolutional neural network, CT image of liver, multiscale, tumor segmentation ---

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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

Cited by 191 publications

References 20 publications

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Automatic segmentation of kidney and liver tumors in CT images

Liver tumor segmentation based on 3D convolutional neural network with dual scale

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