Quantification and visualization of carotid segmentation accuracy and precision using a 2D standardized carotid map

Unsupervised shape-and-texture-based generative adversarial tuning of pre-trained networks for carotid segmentation from 3D ultrasound images

BACKGROUND

Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensitive to the change of carotid atherosclerosis in response to medical/dietary interventions. Manual segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) required to obtain these metrics is time-consuming and prone to observer variability. Although supervised deep-learning segmentation models have been proposed, training of these models requires a sizeable manually segmented training set, making larger clinical studies prohibitive.

PURPOSE

We aim to develop a method to optimize pre-trained segmentation models without requiring manual segmentation to supervise the fine-tuning process.

METHODS

We developed an adversarial framework called the unsupervised shape-and-texture generative adversarial network (USTGAN) to fine-tune a convolutional neural network (CNN) pre-trained on a source dataset for accurate segmentation of a target dataset. The network integrates a novel texture-based discriminator with a shape-based discriminator, which together provide feedback for the CNN to segment the target images in a similar way as the source images. The texture-based discriminator increases the accuracy of the CNN in locating the artery, thereby lowering the number of failed segmentations. Failed segmentation was further reduced by a self-checking mechanism to flag longitudinal discontinuity of the artery and by self-correction strategies involving surface interpolation followed by a case-specific tuning of the CNN. The U-Net was pre-trained by the source dataset involving 224 3DUS volumes with 136, 44, and 44 volumes in the training, validation and testing sets. The training of USTGAN involved the same training group of 136 volumes in the source dataset and 533 volumes in the target dataset. No segmented boundaries for the target cohort were available for training USTGAN. The validation and testing of USTGAN involved 118 and 104 volumes from the target cohort, respectively. The segmentation accuracy was quantified by Dice Similarity Coefficient (DSC), and incorrect localization rate (ILR). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference of DSCs between models and settings, wherep ≤ 0.05 $p\,\le \,0.05$ was considered statistically significant.

RESULTS

USTGAN attained a DSC of85.7 ± 13.0 $85.7\,\pm \,13.0$ % in LIB and86.2 ± 10.6 ${86.2}\,\pm \,{10.6}$ % in MAB, improving from the baseline performance of74.6 ± 30.7 ${74.6}\,\pm \,{30.7}$ % in LIB (p< 10 - 12 $<10^{-12}$ ) and75.7 ± 28.9 ${75.7}\,\pm \,{28.9}$ % in MAB (p< 10 - 12 $<10^{-12}$ ). Our approach outperformed six state-of-the-art domain-adaptation models (MAB:p ≤ 3.63 × 10 - 7 $p \le 3.63\,\times \,10^{-7}$ , LIB:p ≤ 9.34 × 10 - 8 $p\,\le \,9.34\,\times \,10^{-8}$ ). The proposed USTGAN also had the lowest ILR among the methods compared (LIB: 2.5%, MAB: 1.7%).

CONCLUSION

Our framework improves segmentation generalizability, thereby facilitating efficient carotid disease monitoring in multicenter trials and in clinics with less expertise in 3DUS imaging.

Automated classification of ulcerative lesions in small intestine using densenet with channel attention and residual dilated blocks

Objective. Ulceration of the small intestine, which has a high incidence, includes Crohn's disease (CD), intestinal tuberculosis (ITB), primary small intestinal lymphoma (PSIL), cryptogenic multifocal ulcerous stenosing enteritis (CMUSE), and non-specific ulcer (NSU). However, the ulceration morphology can easily be misdiagnosed through enteroscopy.Approach. In this study, DRCA-DenseNet169, which is based on DenseNet169, with residual dilated blocks and a channel attention block, is proposed to identify CD, ITB, PSIL, CMUSE, and NSU intelligently. In addition, a novel loss function that incorporates dynamic weights is designed to enhance the precision of imbalanced datasets with limited samples. DRCA-Densenet169 was evaluated using 10883 enteroscopy images, including 5375 ulcer images and 5508 normal images, which were obtained from the Shanghai Changhai Hospital.Main results. DRCA-Densenet169 achieved an overall accuracy of 85.27% ± 0.32%, a weighted-precision of 83.99% ± 2.47%, a weighted-recall of 84.36% ± 0.88% and a weighted-F1-score of 84.07% ± 2.14%.Significance. The results demonstrate that DRCA-Densenet169 has high recognition accuracy and strong robustness in identifying different types of ulcers when obtaining immediate and preliminary diagnoses.

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images

Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.

Three-dimensional ultrasound assessment of effects of therapies on carotid atherosclerosis using vessel wall thickness maps

We present a new method for assessing the effects of therapies on atherosclerosis, by measuring the weighted average of carotid vessel-wall-plus-plaque thickness change (ΔVWT¯_Weighted) in 120 patients randomized to pomegranate juice/extract versus placebo. Three-dimensional ultrasound images were acquired at baseline and one year after. Three-dimensional VWT maps were reconstructed and then projected onto a carotid template to obtain two-dimensional VWT maps. Anatomic correspondence on the two-dimensional VWT maps was optimized to reduce misalignment for the same subject and across subjects. A weight was computed at each point on the two-dimensional VWT map to highlight anatomic locations likely to exhibit plaque progression/regression, resulting in ΔVWT¯_Weighted for each subject. The weighted average of VWT-Change measured from the two-dimensional VWT maps with correspondence alignment (ΔVWT¯_Weighted,MDL) detected a significant difference between the pomegranate and placebo groups (P = 0.008). This method improves the cost-effectiveness of proof-of-concept studies involving new therapies for atherosclerosis.

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

PURPOSE

Vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT) measured from three-dimensional (3D) ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification.

METHODS

Segmentation for CCA and ICA was performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting.

RESULTS

Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA.

CONCLUSIONS

The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

Area-Preserving Mapping of 3D Carotid Ultrasound Images Using Density-Equalizing Reference Map

Atherosclerotic plaques are focal and tend to occur at arterial bends and bifurcations. To quantitatively monitor the local changes in the carotid vessel-wall-plus-plaque thickness (VWT) and compare the VWT distributions for different patients or for the same patients at different ultrasound scanning sessions, a mapping technique is required to adjust for the geometric variability of different carotid artery models. In this work, we propose a novel method called density-equalizing reference map (DERM) for mapping 3D carotid surfaces to a standardized 2D carotid template, with an emphasis on preserving the local geometry of the carotid surface by minimizing the local area distortion. The initial map was generated by a previously described arc-length scaling (ALS) mapping method, which projects a 3D carotid surface onto a 2D non-convex L-shaped domain. A smooth and area-preserving flattened map was subsequently constructed by deforming the ALS map using the proposed algorithm that combines the density-equalizing map and the reference map techniques. This combination allows, for the first time, one-to-one mapping from a 3D surface to a standardized non-convex planar domain in an area-preserving manner. Evaluations using 20 carotid surface models show that the proposed method reduced the area distortion of the flattening maps by over 80% as compared to the ALS mapping method. The proposed method is capable of improving the accuracy of area estimation for plaque regions without compromising inter-scan reproducibility.

Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics

Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.

Sensitive three-dimensional ultrasound assessment of carotid atherosclerosis by weighted average of local vessel wall and plaque thickness change

PURPOSE

Vitamin B deficiency has been identified as a risk factor for vascular events. However, the reduction of vascular events was not shown in large randomized controlled trials evaluating B-Vitamin therapy. There is an important requirement to develop sensitive biomarkers to be used as efficacy targets for B-Vitamin therapy as well as other dietary treatments and lifestyle regimes that are being developed. Carotid vessel-wall-plus-plaque thickness change (VWT-Change) measured from 3D ultrasound has been shown to be sensitive to atorvastatin therapies in previous studies. However, B-Vitamin treatment is expected to confer a smaller beneficial effect in carotid atherosclerosis than the strong dose of atorvastatin. This paper introduces a sensitive atherosclerosis biomarker based on the weighted mean VWT-Change measurement from 3D ultrasound with a purpose to detect statistically significant effect of B-Vitamin therapy.

METHODS

Of the 56 subjects analyzed in this study, 27 were randomized to receive a B-Vitamin tablet daily and 29 received a placebo tablet daily. Participants were scanned at baseline and 1.9 ± 0.8 yr later. The 3D VWT map at each scanning session was computed by matching the outer wall and lumen surfaces on a point-by-point basis. The 3D annual VWT-Change maps were obtained by first registering the 3D VWT maps obtained at the baseline and follow-up scanning sessions, and then taking the point-wise difference in VWT and dividing the result by the years elapsed from the baseline to the follow-up scanning session. The 3D VWT-Change maps constructed for all patients were mapped to a 2D carotid template to adjust for the anatomic variability of the arteries. A weight at each point of the carotid template was assigned based on the degree of correlation between the VWT-Change measurements exhibited at that point and the treatment received (i.e., B-Vitamin or placebo) quantified by mutual information. The weighted mean of VWT-Change for each patient, denoted by ΔVWT¯Weighted, was computed according to this weight. T-tests were performed to compare the sensitivity of ΔVWT¯Weighted with existing biomarkers in detecting treatment effects. These biomarkers included changes in intima-media thickness (IMT), total plaque area (TPA), vessel wall volume (VWV), unweighted average of VWT-Change (ΔVWT¯) and a previously described biomarker, denoted by ΔVWT¯S, that quantifies the mean VWT-Change specific to regions of interest identified by a feature selection algorithm.

RESULTS

Among the six biomarkers evaluated, the effect of B Vitamins was detected only by ΔVWT¯Weighted in this cohort (P=4.4×10-3). The sample sizes per treatment group required to detect an effect as large as exhibited in this study were 139, 178, 41 for ΔVWV, ΔVWT¯ and ΔVWT¯Weighted respectively.

CONCLUSION

The proposed weighted mean of VWT-Change is more sensitive than existing biomarkers in detecting treatment effects. This measurement tool will allow for many proof-of-principal studies to be performed for various novel treatments before a more costly study involving a larger population is held to validate the results.

Conformal mapping of carotid vessel wall and plaque thickness measured from 3D ultrasound images

Measurements of vessel-wall-plus-plaque thickness (VWT) from 3D carotid ultrasound have been shown to be sensitive to the effect of pharmaceutical interventions. Since the geometry of carotid arteries is highly subject-specific, quantitative comparison of the distributions of point-wise VWT measured for different patients or for the same patients at different ultrasound scanning sessions requires the development of a mapping strategy to adjust for the geometric variability of different carotid surface models. In this paper, we present an algorithm mapping each 3D carotid surface to a 2D carotid template with an emphasis on preserving the local geometry of the carotid surface by minimizing local angular distortion. The previously described arc-length scaling (AL) approach was applied to generate an initial 2D VWT map. Using results established in the quasi-conformal theory, a new map was computed to compensate for the angular distortion incurred in AL mapping. As the 2D carotid template lies on an L-shaped non-convex domain, one-to-one correspondence of the mapping operation was not guaranteed. To address this issue, an iterative Beltrami differential chopping and smoothing procedure was developed to enforce bijectivity. Evaluations performed in the 20 carotid surface models showed that the reduction in average angular distortion made by the proposed algorithm was highly significant (P = 2.06 × 10^-5). This study is the first study showing that a bijective conformal map to a non-convex domain can be obtained using the iterative Beltrami differential chopping and smoothing procedure. The improved consistency exhibited in the 2D VWT map generated by the proposed algorithm will allow for unbiased quantitative comparisons of VWT as well as local geometric and hemodynamic quantities in population studies.