Liver vessel segmentation based on inter-scale V-Net

Yang, Jinzhu; Fu, Meihan; Hu, Ying

doi:10.3934/mbe.2021217

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…In the last experiment, we compared the performance of the proposed method with some other algorithms described in the related work. We compared indirectly with [4,8,14,15,18] because it was not possible to re-implement or evaluate these methods with our dataset. A direct comparison was made with [16,23].…”

Section: Discussionmentioning

confidence: 99%

“…The method was tested on 3D-IRCADb. Yang et al [15] used an improved V-Net to segment liver blood vessels on the 3D-IRCADb dataset. Improved V-Net is based on inter-scale dense connections in the decoder.…”

Section: Related Workmentioning

confidence: 99%

“…We have compared our segmentation approach with the work of others. Specifically, with works [4,8,[14][15][16]18,23], which combine different approaches to provide liver vessel segmentation.…”

Section: Experiments 5: Comparison Of Between the Proposed Algorithm ...mentioning

confidence: 99%

“…In the case of [4,8,14,15,18], it was impossible to make a fair comparison. The source code for these methods was not available, so it was not possible to train and test these methods on the same enhanced 3D-IRCADb dataset that we used in our work.…”

Section: Experiments 5: Comparison Of Between the Proposed Algorithm ...mentioning

confidence: 99%

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Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function

et al. 2022

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The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experiments 5: Comparison Of Between the Proposed Algorithm ...mentioning

confidence: 99%

Section: Experiments 5: Comparison Of Between the Proposed Algorithm ...mentioning

confidence: 99%

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Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function

et al. 2022

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“…Convolutional neural networks in cerebral vessel segmentation has been explored several groups with good results [25,26] [30,31]. 3D U-net has been used for segmentation of cerebral aneurysms using MR angiogram [32], as well as segmentation of cerebral vessels also using MR angiogram [33].…”

Section: Image Segmentationmentioning

confidence: 99%

Clinical Model for Cerebral Vasospasm Prediction

Hsu¹

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Cerebral vasospasm is a neurosurgical emergency that occurs after aneurysm rupture. Rapid diagnosis allows for initiation of management. CT angiogram is the mainstay diagnostic modality when vasospasm is suspected. Our group aims to develop an automatic segmentation model followed by a clinical model to detect vasospasm using CTa. Unfortunately automatic segmentation outputs can be noisy. Using the predicted segmentation, we propose a clinical model which extracts 13 different clinical features to improve the vasospasm prediction. Our purpose is to develop this Clinical Model and test its robustness to noise and clinical feature ablation with 403 manual segmentations trained using a decision tree classifier. Our model improves F1 scores at certain distributions of noise and with higher levels of noise. We show that a Clinical Model subsequent to an Automatic Segmentation Model may be useful in further optimizing performance of vasospasm diagnosis.

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Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Radiya,

Joakimsen,

Mikalsen

et al. 2023

Eur Radiol

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Objectives Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? Methods A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. Results One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. Conclusion Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. Key Points • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

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Liver vessel segmentation based on inter-scale V-Net

Cited by 16 publications

References 20 publications

Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function

Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function

Clinical Model for Cerebral Vasospasm Prediction

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

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