Partner: Sameer Shah

University of California (US)

Recent publications
1.Byra M., Wu M., Zhang X., Jang H., Ma Y-J., Chang E.Y., Shah S., Du J., Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning, Magnetic Resonance in Medicine, ISSN: 1522-2594, DOI: 10.1002/mrm.27969, Vol.83, No.3, pp.1-14, 2019
Abstract:

Jiang Du, Department of Radiology, University of California, San Diego, CA 92103‐8226. Email: jiangdu@ucsd.edu Funding information The authors acknowledge grant support from GE Healthcare, NIH (1R01 AR062581, 1R01 AR068987 and 1R01 NS092650), and the VA Clinical Science Research & Development Service (1I01CX001388, I21RX002367). Purpose: To develop a deep learning‐based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ, and T∗ 2 parameters, which can be used to assess knee osteoarthritis (OA). Methods: Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ‐weighted MR images. Transfer learning was applied to develop 2D attention U‐Net convolutional neural networks for the menisci segmentation based on each radiologist’s ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ, T∗ 2 relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. Results: The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists’ manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ, and T∗ 2 relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter‐observer variability of 2 radiologists. Conclusion: The proposed deep learning‐based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

Keywords:

deep learning, menisci, osteoarthritis, quantitative MR, segmentation

Affiliations:
Byra M.-IPPT PAN
Wu M.-University of California (US)
Zhang X.-University of California (US)
Jang H.-University of California (US)
Ma Y-J.-University of California (US)
Chang E.Y.-University of California (US)
Shah S.-University of California (US)
Du J.-University of California (US)
2.Byra M., Wan L., Wong J.H., Du J., Shah SB., Andre M.P., Chang E.Y., Quantitative ultrasound and b-mode image texture featurescorrelate with collagen and myelin content in human ulnarnerve fascicles, ULTRASOUND IN MEDICINE AND BIOLOGY, ISSN: 0301-5629, DOI: 10.1016/j.ultrasmedbio.2019.02.019, Vol.45, No.7, pp.1830-1840, 2019
Abstract:

We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (–24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = –0.68), entropy (R = –0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.

Keywords:

NerveQuantitative ultrasoundHigh frequencyHistologyPattern recognitionTexture analysis

Affiliations:
Byra M.-IPPT PAN
Wan L.-University of California (US)
Wong J.H.-University of California (US)
Du J.-University of California (US)
Shah SB.-University of California (US)
Andre M.P.-University of California (US)
Chang E.Y.-University of California (US)

Conference abstracts
1.Byra M., Wu M., Zhang X., Jang H., Ma Y., Chang E., Shah S., Du J., Assessing the performance of knee meniscus segmentation with deep convolutional neural networks in 3D ultrashort echo time (UTE) Cones MR imaging, 27th ISMRM Annual Meeting & Exhibition, 2019-05-11/05-16, Montreal (CA), pp.1-5, 2019