Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets

Sridar, Pradeeba; Kumar, Ashnil; Li, Changyang; Woo, Joyce; Quinton, Ann; Benzie, R.; Peek, Michael; Feng, Dagan; Kumar, Rajeev; Nanan, Ralph; Kim, Jin Man

doi:10.1109/jbhi.2016.2582175

Cited by 14 publications

(9 citation statements)

References 34 publications

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“…Our framework was designed to enable analysis of large scale US data repositories and we demonstrated its utility to retrieve TC images for retrospective analysis. This framework integrates multiple individual modules that we have developed through our prior research [12], [13], [24], [26].…”

Section: Discussionmentioning

confidence: 99%

“…The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. Our method was capable of automatically estimating the thalamus diameter, with the measurement accuracy on par with clinical assessment which has been evaluated in our previous study [26].…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…It was achieved by delineating the head contour using iterative randomized Hough transform [28] and morphological image processing. Further, as the orientation of the fetal brain can vary based on the position of the fetus and the image acquisition angle, a thalamus specific orientation classifier [26] was developed to detect the orientation of the fetal brain. A novel guitar structure was created from the negative space surrounding the thalamus regions to detect the extremities of the thalamus which otherwise cannot be segmented directly.…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…This is the first study to perform computerized measurement of the fetal thalamus diameter. Please refer to our previous work [26] for comparative evaluation of the automated measurements with the manual measurements.

FIGURE 7.The overall process of the thalamic diameter measurement.…”

Section: Methods and Proceduresmentioning

confidence: 99%

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An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images

Sridar

Kumar

Quinton

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Objective: 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.

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Section: Discussionmentioning

confidence: 99%

Section: Methods and Proceduresmentioning

confidence: 99%

Section: Methods and Proceduresmentioning

confidence: 99%

FIGURE 7.The overall process of the thalamic diameter measurement.…”

Section: Methods and Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images

Sridar

Kumar

Quinton

et al. 2019

IEEE J. Transl. Eng. Health Med.

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“…As the boundaries of the thalamus are not well visualised to identify the widest points, we introduced a negative space of the thalamus a ‘guitar’ shape, as shown by the grey shaded region in the Figure (b). The representation of the guitar shape comprises the adjacent anatomical landmarks of the thalamus (cerebellum, cerebellar peduncles and falx), and the wing tips of the guitar correspond to the widest point of the thalamus, a method previously described by Sridar et al The images were selected from a large database using an automatic algorithm and measured manually by two operators (NJK and AEQ) using the guitar shape as a guide.…”

Section: Methodsmentioning

confidence: 99%

Normative ultrasound data of the fetal transverse thalamic diameter derived from 18 to 22 weeks of gestation in routine second‐trimester morphology examinations

Sridar

Kennedy

Quinton

et al. 2020

Australas J Ultrason Med

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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 ‘guitar’ 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.

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Early Pregnancy Fetal Facial Ultrasound Standard Plane‐Assisted Recognition Algorithm

Xue

et al. 2023

J of Ultrasound Medicine

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Objectives Ultrasound screening during early pregnancy is vital in preventing congenital disabilities. For example, nuchal translucency (NT) thickening is associated with fetal chromosomal abnormalities, particularly trisomy 21 and fetal heart malformations. Obtaining accurate ultrasound standard planes of a fetal face during early pregnancy is the key to subsequent biometry and disease diagnosis. Therefore, we propose a lightweight target detection network for early pregnancy fetal facial ultrasound standard plane recognition and quality assessment. Methods First, a clinical control protocol was developed by ultrasound experts. Second, we constructed a YOLOv4 target detection algorithm based on the backbone network as GhostNet and added attention mechanisms CBAM and CA to the backbone and neck structure. Finally, key anatomical structures in the image were automatically scored according to a clinical control protocol to determine whether they were standard planes. Results We reviewed other detection techniques and found that the proposed method performed well. The average recognition accuracy for six structures was 94.16%, the detection speed was 51 FPS, and the model size was 43.2 MB, and a reduction of 83% compared with the original YOLOv4 model was obtained. The precision for the standard median sagittal plane was 97.20%, and the accuracy for the standard retro‐nasal triangle view was 99.07%. Conclusions The proposed method can better identify standard or non‐standard planes from ultrasound image data, providing a theoretical basis for automatic acquisition of standard planes in the prenatal diagnosis of early pregnancy fetuses.

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Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets

Cited by 14 publications

References 34 publications

An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images

An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images

Normative ultrasound data of the fetal transverse thalamic diameter derived from 18 to 22 weeks of gestation in routine second‐trimester morphology examinations

Early Pregnancy Fetal Facial Ultrasound Standard Plane‐Assisted Recognition Algorithm

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