Hepatic vessel segmentation based on animproved 3D region growing algorithm

Zhang, Huahai; Bai, Peirui; Min, Xiaolin; Liu, Qingyi; Ren, Yande; Li, Hui; Li, Yixuan

doi:10.1088/1742-6596/1486/3/032038

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…The traditional machine-learning techniques that are borrowed for vessel segmentation include active contour or level set [4], graph cut [5,6], extreme learning machine [7], vascular filters [8,9,10], and still many others [11,12,13,14]. These approaches can fish out vessels from CT images with moderate accuracy and timesaving.…”

Section: Introductionmentioning

confidence: 99%

Compensating small data with large filters for accurate liver vessel segmentation

Chen

Zhao

Bian

et al. 2023

Preprint

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Background: Segmenting liver vessels on computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. Methods: We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. Results: In experiments on the well-known dataset 3D-IRCADb, the accuracy is improved to 0.99, and the averaged Dice coefficient is lifted to 0.63. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. Conclusion: Sophisticated integration of large number filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.

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Section: Introductionmentioning

confidence: 99%

Compensating small data with large filters for accurate liver vessel segmentation

Chen

Zhao

Bian

et al. 2023

Preprint

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“…The Couinaud representation of the liver is another important feature of clinicians' interest for locating tumors or surgical planning. The evolution of computer science in research on image segmentation has revealed interest in the reconstruction and interpretation of these complex organic structures [1,2]. Their robust extraction from biomedical images requires good acquisition during an imaging examination and advanced image analysis developments.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Liver Anatomy by Combining 3D U-Net Approaches

et al. 2021

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Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.

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Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation

Yan

Zhang

et al. 2021

IEEE J. Biomed. Health Inform.

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Hepatic vessel segmentation based on animproved 3D region growing algorithm

Cited by 5 publications

References 13 publications

Compensating small data with large filters for accurate liver vessel segmentation

Compensating small data with large filters for accurate liver vessel segmentation

Segmentation of Liver Anatomy by Combining 3D U-Net Approaches

Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation

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