Trilateral Attention Network for Real-Time Cardiac Region Segmentation

Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation

Segmentation architectures based on deep learning proficient extraordinary results in medical imaging technologies. Computed tomography (CT) images and Magnetic Resonance Imaging (MRI) in diagnosis and treatment are increasing and significantly support the diagnostic process by removing the bottlenecks of manual segmentation. Cardiac Magnetic Resonance Imaging (CMRI) is a state-of-the-art imaging technique used to acquire vital heart measurements and has received extensive attention from researchers for automatic segmentation. Deep learning methods offer high-precision segmentation but still pose several difficulties, such as pixel homogeneity in nearby organs. The motivated study using the attention mechanism approach was introduced for medical images for automated algorithms. The experiment focuses on observing the impact of the attention mechanism with and without pretrained backbone networks on the UNet model. For the same, three networks are considered: Attention-UNet, Attention-UNet with resnet50 pretrained backbone and Attention-UNet with densenet121 pretrained backbone. The experiments are performed on the ACDC Challenge 2017 dataset. The performance is evaluated by conducting a comparative analysis based on the Dice Coefficient, IoU Coefficient, and cross-entropy loss calculations. The Attention-UNet, Attention-UNet with resnet50 pretrained backbone, and Attention-UNet with densenet121 pretrained backbone networks obtained Dice Coefficients of 0.9889, 0.9720, and 0.9801, respectively, along with corresponding IoU scores of 0.9781, 0.9457, and 0.9612. Results compared with the state-of-the-art methods indicate that the methods are on par with, or even superior in terms of both the Dice coefficient and Intersection-over-union.

Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure

Current noninvasive estimation of right atrial pressure (RAP) by inferior vena cava (IVC) measurement during echocardiography may have significant inter-rater variability due to different levels of observers' experience. Therefore, there is a need to develop new approaches to decrease the variability of IVC analysis and RAP estimation. This study aims to develop a fully automated artificial intelligence (AI)-based system for automated IVC analysis and RAP estimation. We presented a multi-stage AI system to identify the IVC view, select good quality images, delineate the IVC region and quantify its thickness, enabling temporal tracking of its diameter and collapsibility changes. The automated system was trained and tested on expert manual IVC and RAP reference measurements obtained from 255 patients during routine clinical workflow. The performance was evaluated using Pearson correlation and Bland-Altman analysis for IVC values, as well as macro accuracy and chi-square test for RAP values. Our results show an excellent agreement (r=0.96) between automatically computed versus manually measured IVC values, and Bland-Altman analysis showed a small bias of [Formula: see text]0.33 mm. Further, there is an excellent agreement ([Formula: see text]) between automatically estimated versus manually derived RAP values with a macro accuracy of 0.85. The proposed AI-based system accurately quantified IVC diameter, collapsibility index, both are used for RAP estimation. This automated system could serve as a paradigm to perform IVC analysis in routine echocardiography and support various cardiac diagnostic applications.

Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population?

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (p < 0.05) in cross-domain generalization through our approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.

TransCUNet: UNet cross fused transformer for medical image segmentation

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation. However, medical images have complex shapes, the structures of different objects are very different, and most medical datasets are small in scale, making it difficult to train effectively. These problems increase the difficulty of automatic segmentation. To further improve the segmentation performance of the model, we propose a multi-branch network model, called TransCUNet, for segmenting medical images of different modalities. The model contains three structures: cross residual fusion block (CRFB), pyramidal pooling module (PPM) and gated axial-attention, which achieve effective extraction of high-level and low-level features of images, while showing high robustness to different size segmentation objects and different scale datasets. In our experiments, we use four datasets to train, validate and test the models. The experimental results show that TransCUNet has better segmentation performance compared to the current mainstream segmentation methods, and the model has a smaller size and number of parameters, which has great potential for clinical applications.

A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs

Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decision-making, and potentially result in improved patient treatment. The majority of works in the literature discuss training automatic segmentation models using coarse bounding box annotations. However, the granularity of the bounding box annotation could result in the inclusion of a considerable fraction of false positives and negatives at the pixel level that may adversely impact overall semantic segmentation performance. This study evaluates the benefits of using fine-grained annotations of TB-consistent lesions toward training the variants of U-Net models and constructing their ensembles for semantically segmenting TB-consistent lesions in both original and bone-suppressed frontal CXRs. The segmentation performance is evaluated using several ensemble methods such as bitwise- AND, bitwise-OR, bitwise-MAX, and stacking. Extensive empirical evaluations showcased that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0.5743, 95% confidence interval: (0.4055, 0.7431)) compared to the individual constituent models and other ensemble methods. To the best of our knowledge, this is the first study to apply ensemble learning to improve fine-grained TB-consistent lesion segmentation performance.

Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays

Deep learning (DL) methods have demonstrated superior performance in medical image segmentation tasks. However, selecting a loss function that conforms to the data characteristics is critical for optimal performance. Further, the direct use of traditional DL models does not provide a measure of uncertainty in predictions. Even high-quality automated predictions for medical diagnostic applications demand uncertainty quantification to gain user trust. In this study, we aim to investigate the benefits of (i) selecting an appropriate loss function and (ii) quantifying uncertainty in predictions using a VGG16-based-U-Net model with the Monto-Carlo (MCD) Dropout method for segmenting Tuberculosis (TB)-consistent findings in frontal chest X-rays (CXRs). We determine an optimal uncertainty threshold based on several uncertainty-related metrics. This threshold is used to select and refer highly uncertain cases to an expert. Experimental results demonstrate that (i) the model trained with a modified Focal Tversky loss function delivered superior segmentation performance (mean average precision (mAP): 0.5710, 95% confidence interval (CI): (0.4021,0.7399)), (ii) the model with 30 MC forward passes during inference further improved and stabilized performance (mAP: 0.5721, 95% CI: (0.4032,0.7410), and (iii) an uncertainty threshold of 0.7 is observed to be optimal to refer highly uncertain cases.