Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network

Bai, Zhiqi; Jiang, Huiyan; Li, Siqi; Yao, Yu-Dong

doi:10.1109/access.2019.2923218

Cited by 53 publications

(30 citation statements)

References 24 publications

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“…Different from other existing methods, our method has two important characteristics regarding the proposed MFU-net. First, the previous liver tumor segmentation was a two-way process or cascaded approach (18,(29)(30)(31)(32)(33). In other words, tumor segmentation has been done after liver segmentation from the abdominal CT scan image.…”

Section: Discussionmentioning

confidence: 99%

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Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Yang

Lee

et al. 2021

Front. Oncol.

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Segmentation of liver tumors from Computerized Tomography (CT) images remains a challenge due to the natural variation in tumor shape and structure as well as the noise in CT images. A key assumption is that the performance of liver tumor segmentation depends on the characteristics of multiple features extracted from multiple filters. In this paper, we design an enhanced approach based on a two-class (liver, tumor) convolutional neural network that discriminates tumor as well as liver from CT images. First, the contrast and intensity values in CT images are adjusted and high frequencies are removed using Hounsfield units (HU) filtering and standardization. Then, the liver tumor is segmented from entire images with multiple filter U-net (MFU-net). Finally, a quantitative analysis is carried out to evaluate the segmentation results using three different methods: boundary-distance-based metrics, size-based metrics, and overlap-based metrics. The proposed method is validated on CT images from the 3Dircadb and LiTS dataset. The results demonstrate that the multiple filters are useful for extracting local and global feature simultaneously, minimizing the boundary distance errors, and our approach demonstrates better performance in heterogeneous tumor regions of CT images.

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Section: Discussionmentioning

confidence: 99%

“…This reduces the time and effort needed during the liver tumor segmentation process. Second, the final segmentation results do not directly depend on any post-preprocessing method such as level set (34), CRF (35), object-based (36), active contour (29), and so on.…”

Section: Discussionmentioning

confidence: 99%

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Yang

Lee

et al. 2021

Front. Oncol.

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“…Alirr et al [34] achieved a Dice score of 75% by utilizing the traditional method of adaptive thresholding to extract masks of liver tumors. Li et al [35] and Z. Bai et al [36] achieved Dice scores of 65% and 76.5%, respectively, by making some improvements upon the standard U-Net. Z. Bai et al [36] also used an active contour model (ACM) to refine the tumors segmentation.…”

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 99%

“…Li et al [35] and Z. Bai et al [36] achieved Dice scores of 65% and 76.5%, respectively, by making some improvements upon the standard U-Net. Z. Bai et al [36] also used an active contour model (ACM) to refine the tumors segmentation. Similarly, Budak et al [37] achieved a Dice score of 63.4%…”

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 99%

A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

et al. 2021

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Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale features for different tumor sizes. Moreover, the utilization of residual connections and residual blocks helps to extract rich features with a reduced number of parameters. Moreover, the proposed work requires no post-processing techniques to refine the segmentation. The proposed work was evaluated using the 3DIRCADb dataset and achieved a Dice score of 77.15% and 93% accuracy.

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“…Bai et al [25] proposed the Multi-scale candidate generation (MCG) for the liver tumor segmentation approach on CT images. They utilized as an active contour model and 3D fractal residual network in a coarse to fine-tune the liver cancer cells.…”

Section: Background Survey and Importance Feature Of This Researchmentioning

confidence: 99%

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

et al. 2020

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Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.

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Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network

Cited by 53 publications

References 24 publications

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

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