8 research outputs found
Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets
Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks
Quinton, AE ORCiD: 0000-0001-6585-7468Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen. Also, the whole image does not encode sufficient local information about structures to identify different structures in different locations. Here we propose a method to automatically classify 14 different fetal structures in 2-D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image. Our method trains two feature extractors by fine-tuning pre-trained convolutional neural networks with the whole ultrasound fetal images and the discriminant regions of the fetal structures found in the whole image. The novelty of our method is in integrating the classification decisions made from the global and local features without relying on priors. In addition, our method can use the classification outcome to localize the fetal structures in the image. Our experiments on a data set of 4074 2-D ultrasound images (training: 3109, test: 965) achieved a mean accuracy of 97.05%, mean precision of 76.47% and mean recall of 75.41%. The Cohen Îș of 0.72 revealed the highest agreement between the ground truth and the proposed method. The superiority of the proposed method over the other non-fusion-based methods is statistically significant (p < 0.05). We found that our method is capable of predicting images without ultrasound scanner overlays with a mean accuracy of 92%. The proposed method can be leveraged to retrospectively classify any ultrasound images in clinical research
An automated framework for large scale retrospective analysis of ultrasound images
Kennedy, NJ ORCiD: 0000-0003-0801-6317; Quinton, AE ORCiD: 0000-0001-6585-7468Objective: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. Method: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B)-mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. Results: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. Conclusion: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research. © 2013 IEEE
A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm
The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospective measurement of fetal thalamus diameter (FTD). Unfortunately, FTD is not measured in routine 2D ultrasound (2D-US) screenings of fetuses. Manual measurement of FTD is a laborious, difficult, and error-prone process because the thalamus lacks well-defined boundaries in 2D-US images of the fetal brain as it has a similar echogenicity to the surrounding brain tissue. Traditional methods based on statistical shape models (SSMs) perform poorly in measuring FTD due to the noisy textures and fuzzy edges of the fetal thalamus in 2D-US images of the fetal brain. To overcome these difficulties, we propose a deep learning-based automatic FTD measurement algorithm, FTDNet. FTDNet measures FTD by learning to directly detect the measurement landmarks through supervised learning. The algorithm first detects the region of the brain that contains the thalamus structure, and then focuses on processing that region for FTD landmark detection. Our FTD dataset, developed through a consensus between two ultrasonographers, contains 1,111 pairs of landmark coordinates for measuring FTD and verified bounding boxes surrounding the fetal thalamus. To assess FTDNetâs measurement consistency compared to the ground truth, we used the intraclass correlation coefficient (ICC). FTDNet achieved an ICC score of 0.734, significantly outperforming the prior SSM method and other baseline comparison methods. Our findings are an important step forward in understanding the maternal factors which influence fetal brain development.Clinical relevanceâ This work proposes an end-to-end thalamus detection and measurement algorithm for measuring fetal thalamus diameter. Our work represents a significant step in the research of how maternal factors can impact fetal thalamus development. The development of an automatic and accurate method for measuring FTD through deep learning has the potential to greatly advance this field of study
Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function
The measurement of fetal thalamus diameter (FTD) and fetal head circumference (FHC) are crucial in identifying abnormal fetal thalamus development as it may lead to certain neuropsychiatric disorders in later life. However, manual measurements from 2D-US images are laborious, prone to high inter-observer variability, and complicated by the high signal-to-noise ratio nature of the images. Deep learning-based landmark detection approaches have shown promise in measuring biometrics from US images, but the current state-of-the-art (SOTA) algorithm, BiometryNet, is inadequate for FTD and FHC measurement due to its inability to account for the fuzzy edges of these structures and the complex shape of the FTD structure. To address these inadequacies, we propose a novel Swoosh Activation Function (SAF) designed to enhance the regularization of heatmaps produced by landmark detection algorithms. Our SAF serves as a regularization term to enforce an optimum mean squared error (MSE) level between predicted heatmaps, reducing the dispersiveness of hotspots in predicted heatmaps. Our experimental results demonstrate that SAF significantly improves the measurement performances of FTD and FHC with higher intraclass correlation coefficient scores in FTD and lower mean difference scores in FHC measurement than those of the current SOTA algorithm BiometryNet. Moreover, our proposed SAF is highly generalizable and architecture-agnostic. The SAFâs coefficients can be configured for different tasks, making it highly customizable. Our study demonstrates that the SAF activation function is a novel method that can improve measurement accuracy in fetal biometry landmark detection. This improvement has the potential to contribute to better fetal monitoring and improved neonatal outcomes
Normative ultrasound data of the fetal transverse thalamic diameter derived from 18 to 22 weeks of gestation in routine secondâtrimester morphology examinations
© 2020 Australasian Society for Ultrasound in Medicine Introduction: The thalamus is important for a wide range of sensorimotor and neuropsychiatric functions. Departure from normal reference values of the thalamus may be a biomarker for differences in neurodevelopment outcomes and brain anomalies perinatally. Antenatal measurement of thalamus is not currently included in routine fetal ultrasound as differentiation of thalamic borders is difficult. The aim of this work was to present a method to standardise the thalamus measure and provide normative data of the fetal transverse thalamic diameter between 18 and 22 weeks of gestational age. Methods: Transverse thalamic diameter was measured by two sonographers on 1,111 stored ultrasound images at the standard transcerebellar plane. A âguitarâ shape representative structure is presented to demarcate the thalamic diameter. The relationship of the transverse thalamic diameter with gestational age, head circumference and transcerebellar diameter using linear regression modelling was assessed, and the mean of the thalamic diameter was calculated and plotted as a reference chart. Results: Transverse thalamic diameter increased significantly with increasing gestational age, head circumference, and transcerebellar diameter linearly, and normal range thalamic charts are presented. The guitar shape provided good reproducibility of thalamic diameter measures. Conclusion: Measuring thalamus size in antenatal ultrasound examinations with reference to normative charts could be used to assess midline brain structures and predict neurodevelopment disorders and potentially brain anomalies
Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images Using Convolutional Neural Networks
Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00%, 28.15, 86.00%, and 90.00%, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p < 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale
Normative ultrasound data of the fetal transverse thalamic diameter derived from 18 to 22 weeks of gestation in routine second-trimester morphology examinations
© 2020 Australasian Society for Ultrasound in Medicine Introduction: The thalamus is important for a wide range of sensorimotor and neuropsychiatric functions. Departure from normal reference values of the thalamus may be a biomarker for differences in neurodevelopment outcomes and brain anomalies perinatally. Antenatal measurement of thalamus is not currently included in routine fetal ultrasound as differentiation of thalamic borders is difficult. The aim of this work was to present a method to standardise the thalamus measure and provide normative data of the fetal transverse thalamic diameter between 18 and 22 weeks of gestational age. Methods: Transverse thalamic diameter was measured by two sonographers on 1,111 stored ultrasound images at the standard transcerebellar plane. A âguitarâ shape representative structure is presented to demarcate the thalamic diameter. The relationship of the transverse thalamic diameter with gestational age, head circumference and transcerebellar diameter using linear regression modelling was assessed, and the mean of the thalamic diameter was calculated and plotted as a reference chart. Results: Transverse thalamic diameter increased significantly with increasing gestational age, head circumference, and transcerebellar diameter linearly, and normal range thalamic charts are presented. The guitar shape provided good reproducibility of thalamic diameter measures. Conclusion: Measuring thalamus size in antenatal ultrasound examinations with reference to normative charts could be used to assess midline brain structures and predict neurodevelopment disorders and potentially brain anomalies