Quantification of carotid vessel wall and plaque thickness change using 3D ultrasound images
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Quantitative measurements of carotid plaque burden progression or regression are important in monitoring patients and in evaluation of new treatment options. 3D ultrasound (US) has been used to monitor the progression or regression of carotid artery plaques. This paper reports on the development and application of a method used to analyze changes in carotid plaque morphology from 3D US. The technique used is evaluated using manual segmentations of the arterial wall and lumen from 3D US images acquired in two imaging sessions. To reduce the effect of segmentation variability, segmentation was performed five times each for the wall and lumen. The mean wall and lumen surfaces, computed from this set of five segmentations, were matched on a point-by-point basis, and the distance between each pair of corresponding points served as an estimate of the combined thickness of the plaque, intima, and media (vessel-wall-plus-plaque thickness or VWT). The VWT maps associated with the first and the second US images were compared and the differences of VWT were obtained at each vertex. The 3D VWT and VWT-Change maps may provide important information for evaluating the location of plaque progression in relation to the localized disturbances of flow pattern, such as oscillatory shear, and regression in response to medical treatments.Keywords:
Lumen (anatomy)
3D ultrasound
Segmentation-based object categorization
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This chapter discusses current segmentation practices in relation to the criteria of good segmentation. Firms use a zillion different segmentation models. Before introducing CUBEical segmentation it is worth taking a look at some commonly used segmentation models and analysing how well they fit these criteria in order to fully understand the need for a new segmentation framework. One of the segmentations is demo-firmo-graphic segmentation that unfortunately opens the door for the registration trap – the uncritical use of data already registered and available inside the firm. Another one is campaign segmentation that typically sticks to demo-firmo-graphic customer data but instead of segmenting customers they are kept unsegmented in a database from which customers are picked and grouped on an ad hoc basis when running a specific marketing campaign.
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Purpose: We examined postoperative stent and lumen expansions after carotid artery stenting (CAS) in patients with carotid artery stenosis. Furthermore, we investigated factors influencing the stent and lumen expansions in a follow-up period.
Lumen (anatomy)
Carotid stenting
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Previous reports suggest that vessel remodeling is the most important factor in late lumen loss in non-stented lesions, but because results of directional coronary atherectomy (DCA) show that increased plaque area (PA) is also important, the aim of this study was to redefine the mechanism of late lumen loss after DCA. One hundred and twenty lesions that underwent DCA with intravascular ultrasound (IVUS) guidance and serial IVUS analysis were studied, and vessel area (VA), lumen area (LA), PA (VA-LA) and corrected values (each value divided by the value of VA pre procedure to correct the vessel size) were analyzed. During follow-up, corrected VA (cVA) decreased by 0.058 +/- 0.191, whereas corrected PA (cPA) increased by 0.087 +/- 0.159. Though the %PA (PA/VA) after the procedure showed significant negative correlation with the subsequent change in cPA, it did not correlate with the subsequent change in cVA. In conclusions, the mechanism of late lumen loss after DCA consists of both arterial remodeling and plaque proliferation, and the residual %PA after the procedure determines the subsequent lumen loss. With a lower %PA, a change in the PA contributes more to late lumen loss than do changes in VA. With a high %PA, a change in the VA contributes more to late lumen loss.
Lumen (anatomy)
Intravascular Ultrasound
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This study was done to assess how local changes in vessel size, together with plaque load, determine luminal narrowing in atherosclerotic arteries. Fifty-one human femoral arteries were analyzed: 32 postmortem and 19 in vivo by 30-MHz intravascular ultrasound.Histological and intravascular ultrasound cross sections were examined every 0.5 cm over an arterial segment 10 to 15 cm long. In each cross section we measured the lumen area and the area circumscribed by the internal elastic lamina (the IEL area). In each arterial segment, the cross section that contained the least amount of plaque was the reference site. For each cross section, the lumen area stenosis was expressed as percent of the lumen area in the reference site. Similarly, the IEL area was expressed as percent of the IEL area in the reference site (the relative IEL area). There was a significant negative correlation between the relative IEL area and the lumen area stenosis percentage (r = -.62, P < .001 for histology and r = -.66, P < .001 for intravascular ultrasound). When lumen area stenosis was less than about 25%, mainly compensatory enlargement was observed. When lumen area stenosis exceeded about 25%, however, mainly a decrease of the IEL area was observed, which is consistent with arterial wall shrinkage. Furthermore, the increase in plaque area does not account for the total loss of luminal area. There was a moderate correlation between an increase in plaque area and reduction of the corresponding lumen area (r = .49 and r = .56 for histology and intravascular ultrasound, respectively).The decrease in luminal area cannot be attributed to plaque increase alone. Arterial wall shrinkage is a paradoxical mechanism that may contribute to severe luminal narrowing of the atherosclerotic human femoral artery.
Arterial wall
Shrinkage
ATHEROSCLEROTIC VASCULAR DISEASE
Vascular wall
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In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder–decoder structure. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. Consequently, segmentation networks are inefficient and less generalizable across different organs. To solve these problems, we reconstructed the segmentation network based on mathematical methods. We introduced the dynamical systems view into semantic segmentation and proposed a novel segmentation network based on Runge–Kutta methods, referred to hereafter as the Runge–Kutta segmentation network (RKSeg). RKSegs were evaluated on ten organ image datasets from the Medical Segmentation Decathlon. The experimental results show that RKSegs far outperform other segmentation networks. RKSegs use few parameters and short inference time, yet they can achieve competitive or even better segmentation results compared to other models. RKSegs pioneer a new architectural design pattern for segmentation networks.
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Automatic medical volume segmentation often lacks clinical accuracy, necessitating further refinement. In this work, we interactively approach medical volume segmentation via two decoupled modules: interaction-to-segmentation and segmentation propagation. Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then propagates the segmentation(s) to the remaining slices. The user may repeat this process multiple times until a sufficiently high volume segmentation quality is achieved. However, due to the lack of human correction during propagation, segmentation errors are prone to accumulate in the intermediate slices and may lead to sub-optimal performance. To alleviate this issue, we propose a simple yet effective cycle consistency loss that regularizes an intermediate segmentation by referencing the accurate segmentation in the starting slice. To this end, we introduce a backward segmentation path that propagates the intermediate segmentation back to the starting slice using the same propagation network. With cycle consistency training, the propagation network is better regularized than in standard forward-only training approaches. Evaluation results on challenging AbdomenCT-1K and OAI-ZIB datasets demonstrate the effectiveness of our method.
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The goal of interactive segmentation is to assist users in producing segmentation masks as fast and as accurately as possible. Interactions have to be simple and intuitive and the number of interactions required to produce a satisfactory segmentation mask should be as low as possible. In this paper, we propose a novel approach to interactive segmentation based on unconstrained contour clicks for initial segmentation and segmentation refinement. Our method is class-agnostic and produces accurate segmentation masks (IoU > 85%) for a lower number of user interactions than state-of-the-art methods on popular segmentation datasets (COCO MVal, SBD and Berkeley).
Segmentation-based object categorization
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Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans.
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Region growing
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Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.
Segmentation-based object categorization
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