ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

Domain knowledge based comprehensive segmentation of Type-A aortic dissection with clinically-oriented evaluation

Type-A aortic dissection (TAAD) is a cardiac emergency in which rapid diagnosis, prognosis prediction, and surgical planning are critical for patient survival. A comprehensive understanding of the anatomic structures and related features of TAAD patients is the key to completing these tasks. However, due to the emergent nature of this disease and requirement of advanced expertise, manual segmentation of these anatomic structures is not routinely available in clinical practice. Currently, automatic segmentation of TAAD is a focus of the cardiovascular imaging research. However, existing works have two limitations: no comprehensive public dataset and lack of clinically-oriented evaluation. To address these limitations, in this paper we propose imageTAAD, the first comprehensive segmentation dataset of TAAD with clinically-oriented evaluation. The dataset is comprised of 120 cases, and each case is annotated by medical experts with 35 foreground classes reflecting the clinical needs for diagnosis, prognosis prediction and surgical planning for TAAD. In addition, we have identified four key clinical features for clinically-oriented evaluation. We also propose SegTAAD, a baseline method for comprehensive segmentation of TAAD. SegTAAD utilizes two pieces of domain knowledge: (1) the boundaries play a key role in the evaluation of clinical features, and can enhance the segmentation performance, and (2) the tear is located between TL and FL. We have conducted intensive experiments with a variety of state-of-the-art (SOTA) methods, and experimental results have shown that our method achieves SOTA performance on the ImageTAAD dataset in terms of overall DSC score, 95% Hausdorff distance, and four clinical features. In our study, we also found an interesting phenomenon that a higher DSC score does not necessarily indicate better accuracy in clinical feature extraction. All the dataset, code and trained models have been published (Xiaowei, 2024).

Application of dual branch and bidirectional feedback feature extraction networks for real time accurate positioning of stents

The installation of arterial stents refers to the use of stents (also known as vascular stents) to maintain the patency of arteries during the treatment of arterial stenosis or blockage. Arterial stents are typically made of metal or polymer materials and are structured as a mesh that provides support within the blood vessel, preventing it from collapsing again after interventional treatment. The installation of arterial stents is an effective interventional therapy that can significantly improve symptoms caused by arterial stenosis or blockage and enhance the quality of life for patients. Endovascular therapy has become increasingly important for treating both thoracic and abdominal aortic diseases. A critical aspect of this procedure is the precise positioning of stents and complete isolation of the pathology. To enhance stent placement accuracy, we propose a deep learning model called the Double Branch Medical Image Detector (DBMedDet), which offers real-time guidance for stent placement during implantation surgeries. The DBMedDet model features a parallel dual-branch edge feature extraction network, a bidirectional feedback feature fusion neck sub-network, as well as a position detection head and a classification head specifically designed for thoracic and abdominal aortic stents. The model has achieved a detection Mean Average Precision (mAP) of 0.841 (mAP@0.5) and a real-time detection speed of 127 Frames Per Second (FPS). For mAP@0.5, when employing 5-fold cross-validation, DBMedDet demonstrates superior performance compared to several YOLO models, achieving improvements of 4.88% over YOLOv8l, 4.61% over YOLOv8m, 3.20% over YOLOv8s, 6.23% over YOLOv8n, 6.09% over YOLOv10s, 3.92% over YOLOv9s, 3.20% over YOLOv8s, 3.00% over YOLOv7tiny, and 5.01% over YOLOv5s. This study presents a precise and easily implementable method for the automatic detection of stent placement limits in the thoracic and abdominal aorta. The model can be applied in various areas such as coronary intervention therapy, peripheral vascular intervention therapy, cerebrovascular intervention therapy, postoperative monitoring and follow-up, and medical training and education. By utilizing real-time imaging guidance and deep learning models (such as DBMedDet), stent placement procedures in these application areas can be performed with greater precision and safety, thereby enhancing patient treatment outcomes and quality of life.

Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow

Life-threatening acute aortic dissection (AD) demands timely diagnosis for effective intervention. To streamline intrahospital workflows, automated detection of AD in abdominal computed tomography (CT) scans seems useful to assist humans. We aimed at creating a robust convolutional neural network (CNN)-based pipeline capable of real-time screening for signs of abdominal AD in CT. In this retrospective study, abdominal CT data from AD patients presenting with AD and from non-AD patients were collected (n 195, AD cases 94, mean age 65.9 years, female ratio 35.8%). A CNN-based algorithm was developed with the goal of enabling a robust, automated, and highly sensitive detection of abdominal AD. Two sets from internal (n = 32, AD cases 16) and external sources (n = 1189, AD cases 100) were procured for validation. The abdominal region was extracted, followed by the automatic isolation of the aorta region of interest (ROI) and highlighting of the membrane via edge extraction, followed by classification of the aortic ROI as dissected/healthy. A fivefold cross-validation was employed on the internal set, and an ensemble of the 5 trained models was used to predict the internal and external validation set. Evaluation metrics included receiver operating characteristic curve (AUC) and balanced accuracy. The AUC, balanced accuracy, and sensitivity scores of the internal dataset were 0.932 (CI 0.891-0.963), 0.860, and 0.885, respectively. For the internal validation dataset, the AUC, balanced accuracy, and sensitivity scores were 0.887 (CI 0.732-0.988), 0.781, and 0.875, respectively. Furthermore, for the external validation dataset, AUC, balanced accuracy, and sensitivity scores were 0.993 (CI 0.918-0.994), 0.933, and 1.000, respectively. The proposed automated pipeline could assist humans in expediting acute aortic dissection management when integrated into clinical workflows.

CIS-UNet: Multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention

Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to erroneous surgical planning and endograft construction. Previous methods simplified aortic segmentation as a binary image segmentation problem, overlooking the necessity of distinguishing between individual aortic branches. In this paper, we introduce Context-Infused Swin-UNet (CIS-UNet), a deep learning model designed for multi-class segmentation of the aorta and thirteen aortic branches. Combining the strengths of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts a hierarchical encoder-decoder structure comprising a CNN encoder, a symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) module as the bottleneck block. Notably, CSW-SA introduces a unique adaptation of the patch merging layer, distinct from its traditional use in the Swin transformers. CSW-SA efficiently condenses the feature map, providing a global spatial context, and enhances performance when applied at the bottleneck layer, offering superior computational efficiency and segmentation accuracy compared to the Swin transformers. We evaluated our model on computed tomography (CT) scans from 59 patients through a 4-fold cross-validation. CIS-UNet outperformed the state-of-the-art Swin UNetR segmentation model by achieving a superior mean Dice coefficient of 0.732 compared to 0.717 and a mean surface distance of 2.40 mm compared to 2.75 mm. CIS-UNet's superior 3D aortic segmentation offers improved accuracy and optimization for planning endovascular treatments. Our dataset and code will be made publicly available at https://github.com/mirthAI/CIS-UNet.

Exploratory analysis of Type B Aortic Dissection (TBAD) segmentation in 2D CTA images using various kernels

Type-B Aortic Dissection is a rare but fatal cardiovascular disease characterized by a tear in the inner layer of the aorta, affecting 3.5 per 100,000 individuals annually. In this work, we explore the feasibility of leveraging two-dimensional Convolutional Neural Network (CNN) models to perform accurate slice-by-slice segmentation of true lumen, false lumen and false lumen thrombus in Computed Tomography Angiography images. The study performed an exploratory analysis of three 2D U-Net models: the baseline 2D U-Net, a variant of U-Net with atrous convolutions, and a U-Net with a custom layer featuring a position-oriented, partially shared weighting scheme kernel. These models were trained and benchmarked against a state-of-the-art baseline 3D U-Net model. Overall, our U-Net with the VGG19 encoder architecture achieved the best performance score among all other models, with a mean Dice score of 80.48% and an IoU score of 72.93%. The segmentation results were also compared with the Segment Anything Model (SAM) and the UniverSeg models. Our findings indicate that our 2D U-Net models excel in false lumen and true lumen segmentation accuracy while achieving lower false lumen thrombus segmentation accuracy compared to the state-of-the-art 3D U-Net model. The study findings highlight the complexities involved in developing segmentation models, especially for cardiovascular medical images, and emphasize the importance of developing lightweight models for real-time decision-making to improve overall patient care.

[Reinforcement learning-based method for type B aortic dissection localization]

In the segmentation of aortic dissection, there are issues such as low contrast between the aortic dissection and surrounding organs and vessels, significant differences in dissection morphology, and high background noise. To address these issues, this paper proposed a reinforcement learning-based method for type B aortic dissection localization. With the assistance of a two-stage segmentation model, the deep reinforcement learning was utilized to perform the first-stage aortic dissection localization task, ensuring the integrity of the localization target. In the second stage, the coarse segmentation results from the first stage were used as input to obtain refined segmentation results. To improve the recall rate of the first-stage segmentation results and include the segmentation target more completely in the localization results, this paper designed a reinforcement learning reward function based on the direction of recall changes. Additionally, the localization window was separated from the field of view window to reduce the occurrence of segmentation target loss. Unet, TransUnet, SwinUnet, and MT-Unet were selected as benchmark segmentation models. Through experiments, it was verified that the majority of the metrics in the two-stage segmentation process of this paper performed better than the benchmark results. Specifically, the Dice index improved by 1.34%, 0.89%, 27.66%, and 7.37% for each respective model. In conclusion, by incorporating the type B aortic dissection localization method proposed in this paper into the segmentation process, the overall segmentation accuracy is improved compared to the benchmark models. The improvement is particularly significant for models with poorer segmentation performance.

Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions.

Automatic Segmentation of Type A Aortic Dissection on Computed Tomography Images Using Deep Learning Approach

PURPOSE

Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment.

METHODS

This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared.

RESULTS

Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89-6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35-9.48%).

CONCLUSIONS

The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.

A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images

Aortic segmentation from computed tomography (CT) is crucial for facilitating aortic intervention, as it enables clinicians to visualize aortic anatomy for diagnosis and measurement. However, aortic segmentation faces the challenge of variable geometry in space, as the geometric diversity of different diseases and the geometric transformations that occur between raw and measured images. Existing constraint-based methods can potentially solve the challenge, but they are hindered by two key issues: inaccurate definition of properties and inappropriate topology of transformation in space. In this paper, we propose a deformable constraint transport network (DCTN). The DCTN adaptively extracts aortic features to define intra-image constrained properties and guides topological implementation in space to constrain inter-image geometric transformation between raw and curved planar reformation (CPR) images. The DCTN contains a deformable attention extractor, a geometry-aware decoder and an optimal transport guider. The extractor generates variable patches that preserve semantic integrity and long-range dependency in long-sequence images. The decoder enhances the perception of geometric texture and semantic features, particularly for low-intensity aortic coarctation and false lumen, which removes background interference. The guider explores the geometric discrepancies between raw and CPR images, constructs probability distributions of discrepancies, and matches them with inter-image transformation to guide geometric topology in space. Experimental studies on 267 aortic subjects and four public datasets show the superiority of our DCTN over 23 methods. The results demonstrate DCTN's advantages in aortic segmentation for different types of aortic disease, for different aortic segments, and in the measurement of clinical indexes.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection

BACKGROUND

Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.

METHODS

A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively.

RESULTS

2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively.

CONCLUSIONS

Both CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs.

RELEVANCE STATEMENT

Deep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection.

KEY POINTS

• 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.

Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature

Purpose of Review

In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI).

Recent Findings

During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research.

Summary

ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40134-022-00407-8.