Key result
Cascaded deep learning achieves ~97% carotid plaque classification accuracy and improves segmentation versus standard models.
Why the study?
Carotid plaque classification based on ultrasound echogenicity and quantification of plaque burden are crucial in stroke risk assessment, motivating models that leverage synergies between classification and segmentation.
Does a cascaded deep learning framework integrating Masked-ResNet-DS and MedSAM improve the classification and segmentation of carotid plaques from 2D ultrasound images compared to standard models?
Does a cascaded deep learning framework integrating Masked-ResNet-DS and MedSAM improve the classification and segmentation of carotid plaques from 2D ultrasound images compared to standard models?
Effect estimate: Mean F1-score of classification 96.7% (Masked-ResNet-DS) vs 92.1% (best existing MSP-VGG); Dice similarity coefficient (DSC) 86.6% (CAM-MedSAM) vs 83.0% (best baseline nnU-Net)
Absolute Event Rate: 96.7% vs 92.1%
p-value: p=2.77e-83 to 2.58e-19 for DSC improvement statistical significance; p = 2.95e-10 for CAM-MedSAM vs Base-MedSAM
A novel cascaded deep learning framework combining Masked-ResNet-DS and MedSAM significantly improves the accuracy of carotid plaque classification and segmentation from ultrasound images.
May aid automated carotid plaque analysis; leaves open prospective validation for stroke risk stratification.
Carotid plaque classification based on ultrasound echogenicity and quantification of plaque burden are crucial in stroke risk assessment. In this work, we propose a framework that leverages the synergy between classification and segmentation by sharing plaque location information to enhance the performance of both tasks. Our cascaded framework integrates a ResNet-based classifier (Masked-ResNet-DS) with MedSAM, a medically adapted version of the Segment Anything Model for joint classification and segmentation of carotid plaques from 2D ultrasound images. Ground truth boundaries are used to guide region-specific feature pooling in the classifier, helping it focus on plaques during training. Since ground truth boundaries are unavailable at inference, we introduce a two-iteration strategy: the first generates a class activation map (CAM), which is then used for focused pooling in the second iteration to predict plaque type. The CAM is also used as a prompt to guide MedSAM for segmentation. To ensure accurate localization, the CAM is supervised during training using a Dice loss against the segmentation ground truth. Masked-ResNet-DS achieves a mean F1-score of 96.7% in plaque classification, at least 3.2% higher than competing methods. Ablation studies confirm that ground truth-based pooling and CAM supervision both improve classification. CAM-guided MedSAM achieves a Dice similarity coefficient (DSC) of 86.6%, outperforming U-Net and nnU-Net by 5.9% and 3.6%, respectively. In addition, CAM prompts improve MedSAM’s DSC by 2.2%. By sharing plaque location between classification and segmentation, the proposed method improves both tasks and provides a more accurate tool for stroke risk stratification.
No takes yet. Share an insight, caveat, or question.
Ren et al. (2026) studied Patients with carotid artery plaques undergoing 2D carotid ultrasound imaging for plaque echogenicity classification and plaque segmentation (n=925). Cascaded deep learning model combining ResNet-based classifier (Masked-ResNet-DS) and MedSAM segmentation using Class Activation Map guidance vs. Existing plaque classification and segmentation methods including VGG16, SPP-VGG, MSP-VGG, MB-DCNN, RCCM-Net, U-Net, nnU-Net, Base-MedSAM was evaluated on Plaque echogenicity classification accuracy and plaque segmentation quality (Dice similarity coefficient) on 2D carotid ultrasound images (Mean F1-score of classification 96.7% (Masked-ResNet-DS) vs 92.1% (best existing MSP-VGG); Dice similarity coefficient (DSC) 86.6% (CAM-MedSAM) vs 83.0% (best baseline nnU-Net), p=2.77e-83 to 2.58e-19 for DSC improvement statistical significance; p = 2.95e-10 for CAM-MedSAM vs Base-MedSAM). The cascaded deep learning model using ResNet-based classifier with CAM supervision and CAM-guided MedSAM segmentation improved plaque classification F1-score to 96.7% and segmentation Dice coefficient to 86.6%, outperforming existing methods by at least 3.2% and 3.6%, respectively.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: