Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach

Mishra, Deepak; Chaudhury, Santanu; Sarkar, Mukul; Manohar, Sidharth; Soin, Arvinder S.

doi:10.1109/iscas.2018.8351049

Cited by 20 publications

(22 citation statements)

References 11 publications

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“…Mishra et al [ 32 ] also segmented vessels in USG images, but they used CNNs. The CNN’s performance is affected by the quantity and complexity of the training data.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Unfortunately, ultrasound images showing liver vessels are not generally available. In [ 32 ], USG images were divided into overlapping patches. Each patch was classified using the CNN as follows: fully or partially covers the region of the vessel (a positive vessel patch) or does not contain any part of the vessel region (negative non-vessel patch).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Of the 31 articles included in the literature review: 21 studies presented methods using CT images [ 12 , 13 , 15 , 16 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 34 , 35 , 36 , 37 , 38 ] 8 papers concerned approaches that use CTA images [ 7 , 8 , 10 , 11 , 14 , 17 , 18 , 24 ] 2 articles presented methods that use MR images [ 13 , 33 ] 2 studies described approaches using USG images [ 26 , 32 ] 13 papers presented results of research based on publicly available datasets [ 12 , 13 , 15 , 16 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 37 , 38 ] 18 articles describe results obtained using private datasets [ ...…”

Section: Challenges and Conclusionmentioning

confidence: 99%

“…18 articles describe results obtained using private datasets [ 7 , 8 , 9 , 10 , 11 , 14 , 17 , 18 , 19 , 20 , 21 , 22 , 26 , 32 , 33 , 34 , 35 , 36 ]…”

Section: Challenges and Conclusionmentioning

confidence: 99%

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Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review

Ciecholewski

Kassjański

2021

Sensors

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The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.

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“…Mishra et al [ 32 ] also segmented vessels in USG images, but they used CNNs. The CNN’s performance is affected by the quantity and complexity of the training data.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Challenges and Conclusionmentioning

confidence: 99%

“…18 articles describe results obtained using private datasets [ 7 , 8 , 9 , 10 , 11 , 14 , 17 , 18 , 19 , 20 , 21 , 22 , 26 , 32 , 33 , 34 , 35 , 36 ]…”

Section: Challenges and Conclusionmentioning

confidence: 99%

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Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review

Ciecholewski

Kassjański

2021

Sensors

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“…With recent advances in parallel computing, large-scale labelled datasets and advances in deep neural network architectures, deep learning based approaches using convolutional neural networks (CNNs) have been applied to 2D US hepatic vasculature segmentation with growing success. A patchbased CNN method with post-processing using k-means clustering was proposed to detect vessels in abdominal 2D US [15], but UNet [16] methods have proven to achieve higher segmentation performance with small, abdominal 2D US datasets [17] and abdominal 3D US datasets [18]. To date, there are no applications of DL to LUS segmentation present in the literature.…”

Section: Introductionmentioning

confidence: 99%

Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

et al. 2021

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Purpose: Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented. Methods: We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device. Results: We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases. Conclusions: We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline.

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MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

Thomson

Smit

Ivashchenko

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.

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Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach

Cited by 20 publications

References 11 publications

Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review

Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review

Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

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