title: Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes.
abstract: 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).
  
  
  
  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 transverseslice-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 probabilitydensity functions to locally model the speckle statistics within consecutive transverse slices.
  
  
  
  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 ± 0.025, 0.832 ± 0.086, and 0.968 ± 0.008, respectively, when compared to expert manual segmentation.
  
  
  
  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.
  
  
  
  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.
authors.lastname: Thanh Minh Bui
authors.forename: Alain
authors.initials: A
authors.lastname: Coron
authors.forename: Jonathan
authors.initials: J
authors.lastname: Mamou
authors.forename: Emi
authors.initials: E
authors.lastname: Saegusa-Beecroft
authors.forename: Tadashi
authors.initials: T
authors.lastname: Yamaguchi
authors.forename: Eugene
authors.initials: E
authors.lastname: Yanagihara
authors.forename: Junji
authors.initials: J
authors.lastname: Machi
authors.forename: S Lori
authors.initials: SL
authors.lastname: Bridal
authors.forename: Ernest Joseph
authors.initials: EJ
authors.lastname: Feleppa
mesh_terms: D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008197:Lymph Node Excision / Q000379:methods; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000473:pathology; D008207:Lymphatic Metastasis / Q000000981:diagnostic imaging* / Q000473:pathology; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D000069453:Ultrasonic Waves; D014463:Ultrasonography / Q000379:methods*