Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: a review

The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may cause medical complications and large number of death. Ultrasound (US) is a widely used imaging modality, as it captures moving images and image features correlate well with results obtained from other imaging methods. Furthermore, US does not use ionizing radiation and it is economical when compared to other imaging modalities. However, reading US images takes time and the relationship between image and tissue composition is complex. Therefore, the diagnostic accuracy depends on both time taken to read the images and experience of the screening practitioner. Computer support tools can reduce the inter-operator variability with lower subject specific expertise, when appropriate processing methods are used. In the current review, we analysed automatic detection methods for the diagnosis of CAD, MI and carotid atherosclerosis based on thoracic and Intravascular Ultrasound (IVUS). We found that IVUS is more often used than thoracic US for CAD. But for MI and carotid atherosclerosis IVUS is still in the experimental stage. Furthermore, thoracic US is more often used than IVUS for computer aided diagnosis systems

FEATURE EXTRACTION AND WALL MOTION CLASSIFICATION OF 2D STRESS ECHOCARDIOGRAPHY WITH RELEVANCE VECTOR MACHINES

Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date [1]. Here, we propose a novel alternative feature selection approach using combined information from rest and stress sequences for motion classification of stress echocardiography, utilizing a Relevance Vector Machine (RVM) classifier. We describe how the proposed RVM method overcomes difficulties that occur with the existing HMM approach. Overall accuracy with the new method for global wall motion classification using datasets from 173 patients is 93.02%, showing that the proposed method outperforms the current state-of-the-art HMM-based approach (for which global classification accuracy is 84.17%). © 2011 IEEE

Feature extraction and wall motion classification of 2D stress echocardiography with support vector machines

Stress echocardiography is a common clinical procedure for diagnosing heart disease. Clinically, diagnosis of the heart wall motion depends mostly on visual assessment, which is highly subjective and operator-dependent. Introduction of automated methods for heart function assessment have the potential to minimise the variance in operator assessment. Automated wall motion analysis consists of two main steps: (i) segmentation of heart wall borders, and (ii) classification of heart function as either "normal" or "abnormal" based on the segmentation. This paper considers automated classification of rest and stress echocardiography. Most previous approaches to the classification of heart function have considered rest or stress data separately, and have only considered using features extracted from the two main frames (corresponding to the end-of-diastole and end-of-systole). One previous attempt [1] has been made to combine information from rest and stress sequences utilising a Hidden Markov Model (HMM), which has proven to be the best performing approach to date. Here, we propose a novel alternative feature selection approach using combined information from rest and stress sequences for motion classification of stress echocardiography, utilising a Support Vector Machines (SVM) classifier. We describe how the proposed SVM-based method overcomes difficulties that occur with HMM classification. Overall accuracy with the new method for global wall motion classification using datasets from 173 patients is 92.47%, and the accuracy of local wall motion classification is 87.20%, showing that the proposed method outperforms the current state-of-the-art HMM-based approach (for which global and local classification accuracy is 82.15% and 78.33%, respectively). © 2011 SPIE

FEATURE EXTRACTION AND WALL MOTION CLASSIFICATION OF 2D STRESS ECHOCARDIOGRAPHY WITH RELEVANCE VECTOR MACHINES

Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date [1]. Here, we propose a novel alternative feature selection approach using combined information from rest and stress sequences for motion classification of stress echocardiography, utilizing a Relevance Vector Machine (RVM) classifier. We describe how the proposed RVM method overcomes difficulties that occur with the existing HMM approach. Overall accuracy with the new method for global wall motion classification using datasets from 173 patients is 93.02%, showing that the proposed method outperforms the current state-of-the-art HMM-based approach (for which global classification accuracy is 84.17%). © 2011 IEEE