Automatic CT liver Couinaud segmentation based on key bifurcation detection with attentive residual hourglass-based cascaded network

Wang, Manyang; Jin, Renchao; Lu, Jiayi; Song, Enmin; Ma, Guangzhi

doi:10.1016/j.compbiomed.2022.105363

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Based on deep learning, many state-of-the-arts methods [17][18][19][20] were shown to be effective on biomedical image recognition and segmentation. For image recognition, Tung et al [21] made an empirical study to detect the scaphoid fracture.…”

Section: Biomedical Image Recognition and Segmentationmentioning

confidence: 99%

Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules

Tung,

Su,

Liao

et al. 2023

Biomedicines

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Over the past few decades, recognition of early lung cancers was researched for effective treatments. In early lung cancers, the invasiveness is an important factor for expected survival rates. Hence, how to effectively identify the invasiveness by computed tomography (CT) images became a hot topic in the field of biomedical science. Although a number of previous works were shown to be effective on this topic, there remain some problems unsettled still. First, it needs a large amount of marked data for a better prediction, but the manual cost is high. Second, the accuracy is always limited in imbalance data. To alleviate these problems, in this paper, we propose an effective CT invasiveness recognizer by semi-automated segmentation. In terms of semi-automated segmentation, it is easy for doctors to mark the nodules. Just based on one clicked pixel, a nodule object in a CT image can be marked by fusing two proposed segmentation methods, including thresholding-based morphology and deep learning-based mask region-based convolutional neural network (Mask-RCNN). For thresholding-based morphology, an initial segmentation is derived by adaptive pixel connections. Then, a mathematical morphology is performed to achieve a better segmentation. For deep learning-based mask-RCNN, the anchor is fixed by the clicked pixel to reduce the computational complexity. To incorporate advantages of both, the segmentation is switched between these two sub-methods. After segmenting the nodules, a boosting ensemble classification model with feature selection is executed to identify the invasiveness by equalized down-sampling. The extensive experimental results on a real dataset reveal that the proposed segmentation method performs better than the traditional segmentation ones, which can reach an average dice improvement of 392.3%. Additionally, the proposed ensemble classification model infers better performances than the compared method, which can reach an area under curve (AUC) improvement of 5.3% and a specificity improvement of 14.3%. Moreover, in comparison with the models with imbalance data, the improvements of AUC and specificity can reach 10.4% and 33.3%, respectively.

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Section: Biomedical Image Recognition and Segmentationmentioning

confidence: 99%

Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules

Tung,

Su,

Liao

et al. 2023

Biomedicines

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Hepatic and portal vein segmentation with dual‐stream deep neural network

Xu,

Jiang,

et al. 2024

Medical Physics

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BackgroundLiver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time‐consuming, and the labeling results of different clinicians are prone to inconsistencies. Hence, developing an automatic segmentation algorithm for hepatic and PVs on CT images has attracted the attention of researchers. Unfortunately, existing deep learning based automatic segmentation methods are prone to misclassifying peripheral vessels into wrong categories.PurposeThis study aims to provide a fully automatic and robust semantic segmentation algorithm for hepatic and PVs, guiding subsequent preoperative planning. In addition, to address the deficiency of the public dataset for hepatic and PV segmentation, we revise the annotations of the Medical Segmentation Decathlon (MSD) hepatic vessel segmentation dataset and add the masks of the hepatic veins (HVs) and PVs.MethodsWe proposed a structure with a dual‐stream encoder combining convolution and Transformer block, named Dual‐stream Hepatic Portal Vein segmentation Network, to extract local features and long‐distance spatial information, thereby extracting anatomical information of hepatic and portal vein, avoiding misdivisions of adjacent peripheral vessels. Besides, a multi‐scale feature fusion block based on dilated convolution is proposed to extract multi‐scale features on expanded perception fields for local features, and a multi‐level fusing attention module is introduced for efficient context information extraction. Paired t‐test is conducted to evaluate the significant difference in dice between the proposed methods and the comparing methods.ResultsTwo datasets are constructed from the original MSD dataset. For each dataset, 50 cases are randomly selected for model evaluation in the scheme of 5‐fold cross‐validation. The results show that our method outperforms the state‐of‐the‐art Convolutional Neural Network‐based and transformer‐based methods. Specifically, for the first dataset, our model reaches 0.815, 0.830, and 0.807 at overall dice, precision, and sensitivity. The dice of the hepatic and PVs are 0.835 and 0.796, which also exceed the numeric result of the comparing methods. Almost all the p‐values of paired t‐tests on the proposed approach and comparing approaches are smaller than 0.05. On the second dataset, the proposed algorithm achieves 0.749, 0.762, 0.726, 0.835, and 0.796 for overall dice, precision, sensitivity, dice for HV, and dice for PV, among which the first four numeric results exceed comparing methods.ConclusionsThe proposed method is effective in solving the problem of misclassifying interlaced peripheral veins for the HV and PV segmentation task and outperforming the comparing methods on the relabeled dataset.

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Robust and smooth Couinaud segmentation via anatomical structure-guided point-voxel network

Zhang,

Ali,

Liu

et al. 2024

Computers in Biology and Medicine

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Automatic CT liver Couinaud segmentation based on key bifurcation detection with attentive residual hourglass-based cascaded network

Cited by 5 publications

References 20 publications

Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules

Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules

Hepatic and portal vein segmentation with dual‐stream deep neural network

Robust and smooth Couinaud segmentation via anatomical structure-guided point-voxel network

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