Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features

Di, Shuanhu; Zhao, Yuqian; Liao, Miao; Yang, Zhen; Zeng, Ye-zhan

doi:10.1016/j.eswa.2022.117347

Cited by 18 publications

(4 citation statements)

References 31 publications

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“…This helps inhibit the influence of neighboring pixels and greatly alleviates the computational burden. In Di et al (2022), the authors accurately segment liver tumors from CT images by using 3D U-Net to detect liver regions and alleviate the computational cost of the segmentation. Typically, liver regions are first extracted using a 3D U-Net before dividing them into homogeneous superpixels by applying a hierarchical iterative segmentation strategy, which relies on local-information-based simple linear iterative clustering (SLIC).…”

Section: D Inputmentioning

confidence: 99%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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Section: D Inputmentioning

confidence: 99%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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“…Sometimes, these techniques may result in leakage or coarse segmentation due of the noise and rotation. Still, there is scope for improvement in the watershed algorithm to avoid over-segmentation and to reduce the noise effect [25]- [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Liver segmentation using marker controlled watershed transform

Napte

Mahajan

2023

IJECE

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<span lang="EN-US">The largest organ in the body is the liver and primarily helps in metabolism and detoxification. Liver segmentation is a crucial step in liver cancer detection in computer vision-based biomedical image analysis. Liver segmentation is a critical task and results in under-segmentation and over-segmentation due to the complex structure of abdominal computed tomography (CT) images, noise, and textural variations over the image. This paper presents liver segmentation in abdominal CT images using marker-based watershed transforms. In the pre-processing stage, a modified double stage gaussian filter (MDSGF) is used to enhance the contrast, and preserve the edge and texture information of liver CT images. Further, marker controlled watershed transform is utilized for the segmentation of liver images from the abdominal CT images. Liver segmentation using suggested MDSGF and marker-based watershed transform help to diminish the under-segmentation and over-segmentation of the liver object. The performance of the proposed system is evaluated on the LiTS dataset based on Dice score (DS), relative volume difference (RVD), volumetric overlapping error (VOE), and Jaccard index (JI). The proposed method gives (Dice score of 0.959, RVD of 0.09, VOE of 0.089, and JI of 0.921).</span>

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“…Over-segment: the input breast image will be over-segmented into the corresponding superpixel mask which includes prior shape information of tumors. In this study, simple linear iterative clustering (SLIC) algorithm (Achanta et al 2012), a widely used technique in the field of medical image processing (Gao et al 2017, Chandra et al 2022, Di et al 2022, Bao et al 2023 due to its low computational complexity and promising performance, is employed to produce superpixel masks for breast images. The SLIC algorithm first transforms the color space of an image from RGB to CIELAB and then generates homogeneous regions with different sizes by clustering similar pixels.…”

Section: Overviewmentioning

confidence: 99%

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

Lin

Huang

et al. 2023

Phys. Med. Biol.

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Due to the blurry edges and uneven shape of breast tumors, breasttumor segmentation can be a challenging task. Recently, deep convolution networks(DCNs) based approaches achieve satisfying segmentation results. However, thelearned shape information of breast tumors might be lost owing to the successiveconvolution and down-sampling operations, resulting in limited performance. To thisend, we propose a novel Shape-Guided Segmentation (SGS) framework that guidesthe segmentation networks to be shape-sensitive to breast tumors by prior shapeinformation. Different from usual segmentation networks, we guide the networks tomodel shape-shared representation with the assumption that shape information ofbreast tumors can be shared among samples. Specifically, on the one hand, we proposea Shape Guiding Block (SGB) to provide shape guidance through a superpixel poolingunpooling operation and attention mechanism. On the other hand, we introducethe Shared Classification Layer (SCL) to address the problems brought by the SGB,including feature inconsistency and additional computational cost. Additionally, theproposed SGB and SCL can be effortlessly incorporated into mainstream segmentationnetworks (e.g., UNet) to compose the SGS, facilitating compact shape-friendlyrepresentation learning. Experiments conducted on a private dataset and a publicdataset demonstrate the effectiveness of the SGS compared to other advanced methods.The source code is made available at https://github.com/TxLin7/Shape-Seg.

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Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features

Cited by 18 publications

References 31 publications

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Liver segmentation using marker controlled watershed transform

Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

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