Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes

Autor: Junji Machi, S. Lori Bridal, Jonathan Mamou, Thanh Minh Bui, Alain Coron, Tadashi Yamaguchi, Emi Saegusa-Beecroft, Ernest J. Feleppa, Eugene Yanagihara
Rok vydání: 2017
Předmět:
Level set method
Computer science
Ultrasound attenuation
Biomedical Engineering
02 engineering and technology
Sensitivity and Specificity
Pattern Recognition
Automated
030218 nuclear medicine & medical imaging
03 medical and health sciences
Speckle pattern
Imaging
Three-Dimensional
0302 clinical medicine
Neoplasms
Image Interpretation
Computer-Assisted
Parenchyma
0202 electrical engineering
electronic engineering
information engineering
Humans
Scattering
Radiation
Segmentation
Computer vision
Ultrasonography
business.industry
Attenuation
Reproducibility of Results
Image segmentation
Image Enhancement
Quantitative ultrasound
Transverse plane
Ultrasonic Waves
Lymphatic Metastasis
Lymph Node Excision
020201 artificial intelligence & image processing
Lymph Nodes
Artificial intelligence
business
Smoothing
Biomedical engineering
Zdroj: IEEE Transactions on Biomedical Engineering. 64:1579-1591
ISSN: 1558-2531
0018-9294
Popis: Objective: To detect metastases in freshly excised human lymph nodes (LNs) using three-dimensional (3-D), high-frequency, quantitative ultrasound (QUS) methods, the LN parenchyma (LNP) must be segmented to preclude QUS analysis of data in regions outside the LNP and to compensate ultrasound attenuation effects due to overlying layers of LNP and residual perinodal fat (PNF). Methods: After restoring the saturated radio-frequency signals from PNF using an approach based on smoothing cubic splines, the three regions, i.e., LNP, PNF, and normal saline (NS), in the LN envelope data are segmented using a new, automatic, 3-D, three-phase, statistical transverse-slice-based level-set (STS-LS) method that amends Lankton's method. Due to ultrasound attenuation and focusing effects, the speckle statistics of the envelope data vary with imaged depth. Thus, to mitigate depth-related inhomogeneity effects, the STS-LS method employs gamma probability-density functions to locally model the speckle statistics within consecutive transverse slices. Results: Accurate results were obtained on simulated data. On a representative dataset of 54 LNs acquired from colorectal-cancer patients, the Dice similarity coefficient for LNP, PNF, and NS were 0.938 $\pm$ 0.025, 0.832 $\pm$ 0.086, and 0.968 $\pm$ 0.008, respectively, when compared to expert manual segmentation. Conclusion: The STS-LS outperforms the established methods based on global and local statistics in our datasets and is capable of accurately handling the depth-dependent effects due to attenuation and focusing. Significance: This advance permits the automatic QUS-based cancer detection in the LNs. Furthermore, the STS-LS method could potentially be used in a wide range of ultrasound-imaging applications suffering from depth-dependent effects.
Databáze: OpenAIRE
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