Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Behboodi, Bahareh; Rivaz, Hassan; Lalondrelle, Susan; Harris, Emma

doi:10.1109/ius52206.2021.9593671

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…For ultrasound images, automated segmentation approaches have also been proposed using a modified 2D U-Net architecture for segmentation of the uterus [ 30 ]. Patients with uterine fibroids were not specifically considered in that study and although several models were trained at different 2D planes, overall only low Dice scores were reported.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research has already investigated deep learning methods for uterine segmentation, where most of the presented approaches are also based on a U-Net architecture [29][30][31]. In one of these studies, also based on MRI, a 3D U-Net model requiring only minimal user interaction for the segmentation of the uterine cavity and the placenta of pregnant women was presented and evaluated in normal pregnant women and also in women with suspected placental abnormalities.…”

Section: Discussionmentioning

confidence: 99%

“…Very recently, various neural networks have been proposed for uterine segmentation in MRI and ultrasound imaging, where most of them are also based on the U-Net architecture [29][30][31][32]. However, none of these has presented a suitable method for accurate automatic volumetry of the uterus, especially when the evaluation of longitudinal data is required to assess treatment response, such as in patients with uterine fibroids undergoing HIFU therapy.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy

et al. 2023

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Background High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach. Methods A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 patients with uterine fibroids undergoing ultrasound-guided HIFU therapy (HIFU group). Here, preHIFU scans (n = 56), postHIFU imaging maximum one day after HIFU (n = 54), and the last available follow-up examination (n = 53, days after HIFU: 420 ± 377) were included. The training was performed on 80% of the data with fivefold cross-validation. The remaining data were used as a hold-out test set. Ground truth was generated by a board-certified radiologist and a radiology resident. For the assessment of inter-reader agreement, all preHIFU examinations were segmented independently by both. Results High segmentation performance was already observed for the default 3D nnU-Net (mean Dice score = 0.95 ± 0.05) on the validation sets. Since the CBAM nnU-Net showed no significant benefit, the less complex default model was applied to the hold-out test set, which resulted in accurate uterus segmentation (Dice scores: standard group 0.92 ± 0.07; HIFU group 0.96 ± 0.02), which was comparable to the agreement between the two readers. Conclusions This study presents a method for automatic uterus segmentation which allows a fast and consistent assessment of uterine volume. Therefore, this method could be used in the clinical setting for objective assessment of therapeutic response to HIFU therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy

et al. 2023

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“…Then, the processed images are fed into the second stage to remove the unrelated regions in preparation for the final fibroids segmentation in the third stage. Behboodi et al [23] combined 2D U-Net and MobileNet-v2 [24] to make the first fully-automatic segmentation try in ultrasound uterus images. Generally, these methods rely on very large amounts of annotated data to perform segmentation.…”

Section: Related Workmentioning

confidence: 99%

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

Zhang

Yang

et al. 2023

IRBM

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“…Ultrasound imaging is commonly used to diagnose UF as it is non-invasive and readily accessible [28]. However, the skills of ultrasonographers greatly affect the accuracy of the diagnosis, and both falsenegative and false-positive results are possible [29]. The accuracy and speed of UF diagnosis may be improved by using automated systems based on DL techniques.…”

Section: Related Workmentioning

confidence: 99%

Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Shahzad,

Mushtaq,

Sabeeh

et al. 2023

Healthcare

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Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results. In this research, we evaluated state-of-the-art DL architectures VGG16, ResNet50, InceptionV3, and our proposed innovative dual-path deep convolutional neural network (DPCNN) architecture for UF detection tasks. Using preprocessing methods including scaling, normalization, and data augmentation, an ultrasound image dataset from Kaggle is prepared for use. After the images are used to train and validate the DL models, the model performance is evaluated using different measures. When compared to existing DL models, our suggested DPCNN architecture achieved the highest accuracy of 99.8 percent. Findings show that pre-trained deep-learning model performance for UF diagnosis from medical images may significantly improve with the application of fine-tuning strategies. In particular, the InceptionV3 model achieved 90% accuracy, with the ResNet50 model achieving 89% accuracy. It should be noted that the VGG16 model was found to have a lower accuracy level of 85%. Our findings show that DL-based methods can be effectively utilized to facilitate automated UF detection from medical images. Further research in this area holds great potential and could lead to the creation of cutting-edge computer-aided diagnosis systems. To further advance the state-of-the-art in medical imaging analysis, the DL community is invited to investigate these lines of research. Although our proposed innovative DPCNN architecture performed best, fine-tuned versions of pre-trained models like InceptionV3 and ResNet50 also delivered strong results. This work lays the foundation for future studies and has the potential to enhance the precision and suitability with which UF is detected.

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Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Cited by 4 publications

References 13 publications

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks

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