Capsule networks for segmentation of small intravascular ultrasound image datasets

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

CADNet: an advanced architecture for automatic detection of coronary artery calcification and shadow border in intravascular ultrasound (IVUS) images

Intravascular Ultrasound (IVUS) is a medical imaging modality widely used for the detection and treatment of coronary heart disease. The detection of vascular structures is extremely important for accurate treatment procedures. Manual detection of lumen and calcification is very time-consuming and requires technical experience. Ultrasound imaging suffers from the generation of artifacts which obstructs the clear delineation among structures. Considering, the need, to provide special attention to crucial areas, convolutional block attention modules (CBAM) is integrated into an encoder-decoder-based U-Net architecture along with Atrous Spatial Pyramid Pooling (ASPP) to detect vessel components: lumen, calcification and shadow borders. The attention modules prove effective in dealing with areas of special attention by assigning additional weights to crucial channels and preserving spatial features. The IVUS data of 12 patients undergoing the treatment is taken for this study. The novelty of the model design is such that it is able to detect the lumen area in the presence/absence of calcification and bifurcation artifacts too. Also, the model efficiently detects the calcification area even in case of severely complex lesions with shadows behind them. The main contribution of the work is that IVUS images of varying degrees of calcification till 360° are also considered in this work, which is usually neglected in previous studies. The experimental results of 1097 IVUS images of 12 patients resulted in meanIoU (0.7894 ± 0.011), Dice Coefficient (0.8763 ± 0.070), precision (0.8768 ± 0.069) and recall (0.8774 ± 0.071) of the proposed model CADNet which show the model's effectiveness relative to other state-of-the art methods.

Diagnostic performance of IVUS-FFR analysis based on generative adversarial network and bifurcation fractal law for assessing myocardial ischemia

Background

IVUS-based virtual FFR (IVUS-FFR) can provide additional functional assessment information to IVUS imaging for the diagnosis of coronary stenosis. IVUS image segmentation and side branch blood flow can affect the accuracy of virtual FFR. The purpose of this study was to evaluate the diagnostic performance of an IVUS-FFR analysis based on generative adversarial networks and bifurcation fractal law, using invasive FFR as a reference.

Method

In this study, a total of 108 vessels were retrospectively collected from 87 patients who underwent IVUS and invasive FFR. IVUS-FFR was performed by analysts who were blinded to invasive FFR. We evaluated the diagnostic performance and computation time of IVUS-FFR, and compared it with that of the FFR-branch (considering side branch blood flow by manually extending the side branch from the bifurcation ostia). We also compared the effects of three bifurcation fractal laws on the accuracy of IVUS-FFR.

Result

The diagnostic accuracy, sensitivity, and specificity for IVUS-FFR to identify invasive FFR≤0.80 were 90.7% (95% CI, 83.6–95.5), 89.7% (95% CI, 78.8–96.1), 92.0% (95% CI, 80.8–97.8), respectively. A good correlation and agreement between IVUS-FFR and invasive FFR were observed. And the average computation time of IVUS-FFR was shorter than that of FFR-branch. In addition to this, we also observe that the HK model is the most accurate among the three bifurcation fractal laws.

Conclusion

Our proposed IVUS-FFR analysis correlates and agrees well with invasive FFR and shows good diagnostic performance. Compared with FFR-branch, IVUS-FFR has the same level of diagnostic performance with significantly lower computation time.

3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation

Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences.Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction.Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%.Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.

A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images

Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.

A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence

In vivo application of intravascular photoacoustic (IVPA) imaging for coronary arteries is hampered by motion artifacts associated with the cardiac cycle. Gating is a common strategy to mitigate motion artifacts. However, a large amount of diagnostically valuable information might be lost due to one frame per cycle. In this work, we present a deep learning-based method for directly correcting motion artifacts in non-gated IVPA pullback sequences. The raw signal frames are classified into dynamic and static frames by clustering. Then, a neural network named Motion Artifact Correction (MAC)-Net is designed to correct motion in dynamic frames. Given the lack of the ground truth information on the underlying dynamics of coronary arteries, we trained and tested the network using a computer-generated dataset. Based on the results, it has been observed that the trained network can directly correct motion in successive frames while preserving the original structures without discarding any frames. The improvement in the visual effect of the longitudinal view has been demonstrated based on quantitative evaluation of the inter-frame dissimilarity. The comparison results validated the motion-suppression ability of our method comparable to gating and image registration-based non-learning methods, while maintaining the integrity of the pullbacks without image preprocessing. Experimental results from in vivo intravascular ultrasound and optical coherence tomography pullbacks validated the feasibility of our method in the in vivo intracoronary imaging scenario.

Ultrasonic breast tumor extraction based on adversarial mechanism and active contour

BACKGROUND AND OBJECTIVE

Breast cancer is a high incidence of gynecological diseases; breast ultrasound screening can effectively reduce the mortality rate of breast cancer. In breast ultrasound images, the localization and segmentation of tumor lesions are important steps for the extraction of lesions, which helps clinicians evaluate breast lesions quantitatively and makes better clinical diagnosis of the disease. However, the segmentation of breast lesions is difficult due to the blurred and uneven edges of some lesions. In this paper, we propose a segmentation framework combining active contour module and deep learning adversarial mechanism and apply it for the segmentation of breast tumor lesions.

METHOD

We use a conditional adversarial network as the main framework. The generator is a segmentation network consisting of a Deformed U-Net and an active contour module. Here, the Deformed U-Net performs pixel-level segmentation for breast ultrasound images. The active contour module refines the tumor lesion edges, and the refined result provides loss information for Deformed U-Net. Therefore, the Deformed U-Net can better classify the edge pixels. The discriminator is the Markov discriminator; this discriminator provides loss feedback for the segmentation network. We cross-train the discriminator and segmentation network to implement Adversarial Mechanism for getting a more optimized segmentation network.

RESULTS

The segmentation performance of the segmentation network for breast ultrasound images is improved by adding a Markov discriminator to provide discriminant loss training. The proposed method for segmenting the tumor lesions in breast ultrasound image obtains dice coefficient: 89.7%, accuracy: 98.1%, precision: 86.3%, mean-intersection-over-union: 82.2%, recall: 94.7%, specificity: 98.5% and F1score: 89.7%.

CONCLUSION

Comparing with traditional methods, the proposed method gives better performance. The experimental results show that the proposed method can effectively segment the lesions in breast ultrasound images, and then assist doctors to realize the diagnosis of breast lesions.