Two-stage ultrasound image segmentation using U-Net and test time augmentation

Amiri, Mina; Brooks, Rupert; Behboodi, Bahareh; Rivaz, Hassan

doi:10.1007/s11548-020-02158-3

Cited by 83 publications

(37 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was possible because similar errors were also incorporated in training which ensured that the network could handle these errors. Qiu et al 13 and Amiri et al 30 utilized another CNN to identify the correct bounding box prior to performing the segmentation. This would be challenging in our diverse dataset as we observe from Figure 7(a) where the automatic U-Net could not even detect the correct location of the plaque in the B-mode image.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture

et al. 2020

View full text Add to dashboard Cite

Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which the dilated convolution layers were used in the bottleneck. Both a fully automatic and a semi-automatic approach with a bounding box was implemented. The performance degradation in plaque segmentation due to errors in the bounding box is quantified. We found that the bounding box significantly improved the performance of the networks with U-Net Dice coefficients of 0.48 for automatic and 0.83 for semi-automatic segmentation of plaque. Similar results were also obtained for the dilated U-Net with Dice coefficients of 0.55 for automatic and 0.84 for semi-automatic when compared to manual segmentations of the same plaque by an experienced sonographer. A 5% error in the bounding box in both dimensions reduced the Dice coefficient to 0.79 and 0.80 for U-Net and dilated U-Net respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture

et al. 2020

View full text Add to dashboard Cite

show abstract

“…More recently, this procedure has been applied to medical image segmentation tasks to improve segmentation accuracy. The final segmented label was computed as an average or as a pixel-wise majority voting of the predicted pixels [53][54][55][56][57] . The disadvantage of TTA is its computational cost since the inference is performed many times depending on the number of augmentations.…”

Section: Related Workmentioning

confidence: 99%

CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation

Al‐masni

Kim

2021

Preprint

View full text Add to dashboard Cite

Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

show abstract

“…Artifacts, speckle noise, and lesion shape similarities can be found in ultrasonic images. Breast lesion segmentation remains an unsolved problem as a result of these difficulties [ 15 , 16 , 17 ]. Existing studies lack vigor, intensity inhomogeneity, artifact removal, and precise lesion segmentation [ 15 , 16 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Breast lesion segmentation remains an unsolved problem as a result of these difficulties [ 15 , 16 , 17 ]. Existing studies lack vigor, intensity inhomogeneity, artifact removal, and precise lesion segmentation [ 15 , 16 , 18 , 19 ]. Because of the deep convolutional process, which extracts rich feature vectors, deep learning-based approaches for semantic segmentation and classification have gained popularity [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

et al. 2021

View full text Add to dashboard Cite

Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

show abstract

Two-stage ultrasound image segmentation using U-Net and test time augmentation

Cited by 83 publications

References 20 publications

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture

CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Contact Info

Product

Resources

About