Ovarian volume assessment is the measurement of the size of ovaries during an Ultrasound (US) in order to estimate the ovarian reserve. Since the ovarian reserve is used in calculating a woman’s reproductive age and is also a diagnostic criterion for polycystic ovary syndrome (PCOS), it is imperative that it is measured accurately. Furthermore, ovarian rendering has clinical significance in terms of assessing ovarian anomalies (ovarian surface epithelial cells). Thus if the spacing in the US volume is high along one direction, reducing the spacing would greatly help in both the accurate measurement of the ovarian volume as well as surface assessment. In this paper, we aim to address this problem by developing a deep learning method for super-resolving 3D US data along the axial direction. On the collected dataset, our method has achieved high PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) values, and has also resulted in a 54% improvement in ovarian volume computation accuracy. Furthermore, our solution has improved the quality of the 3D rendering of the ovary, and has also reduced the problem of fused follicles in segmentation. This proves the viability of our approach for clinical diagnostic assessment.
Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.
Segmentation of left ventricle (LV) in contrast-enhanced cardiac MR images is a challenging task because of high variability in the image intensity. This is due to a) wash-in and wash-out of the contrast agent over time and b) poor contrast around the epicardium (outer wall) region. Current approaches for segmentation of the endocardium (inner wall) usually involve application of a threshold within the region of interest, followed by refinement techniques like active contours. A limitation of this method is under-segmentation of the inner wall because of gradual loss of contrast at the wall boundary. On the other hand, the challenge in outer wall segmentation is the lack of reliable boundaries because of poor contrast. There are four main contributions in this paper to address the aforementioned issues. First, a seed image is selected using variance based approach on 4D time-frame images over which initial endocardium and epicardium is segmented. Secondly, we propose a patch based feature which overcomes the problem of gradual contrast loss for LV endocardium segmentation. Third, we propose a novel Iterative-Edge-Refinement (IER) technique for epicardium segmentation. Fourth, we propose a greedy search algorithm for propagating the initial contour segmented on seed-image across other time frame images. We have experimented our technique on five contrast-enhanced cardiac MR Datasets (4D) having a total of 1097 images. The segmentation results for all 1097 images have been visually inspected by a clinical expert and have shown good accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.