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Temporal‐based needle segmentation algorithm for transrectal ultrasound prostate biopsy procedures
Author(s) -
Cool Derek W.,
Gardi Lori,
Romagnoli Cesare,
Saikaly Manale,
Izawa Jonathan I.,
Fenster Aaron
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3360440
Subject(s) - biopsy , prostate biopsy , segmentation , ultrasound , radiology , medicine , hough transform , 3d ultrasound , image segmentation , prostate , computer science , artificial intelligence , image (mathematics) , cancer
Purpose: Automatic identification of the biopsy‐core tissue location during a prostate biopsy procedure would provide verification that targets were adequately sampled and would allow for appropriate intraprocedure biopsy target modification. Localization of the biopsy core requires accurate segmentation of the biopsy needle and needle tip from transrectal ultrasound (TRUS) biopsy images. A temporal‐based TRUS needle segmentation algorithm was developed specifically for the prostate biopsy procedure to automatically identify the TRUS image containing the biopsy needle from a collection of 2D TRUS images and to segment the biopsy‐core location from the 2D TRUS image. Methods: The temporal‐based segmentation algorithm performs a temporal analysis on a series of biopsy TRUS images collected throughout needle insertion and withdrawal. Following the identification of points of needle insertion and retraction, the needle axis is segmented using a Hough transform‐based algorithm, which is followed by a temporospectral TRUS analysis to identify the biopsy‐needle tip. Validation of the temporal‐based algorithm is performed on 108 TRUS biopsy sequences collected from the procedures of ten patients. The success of the temporal search to identify the proper images was manually assessed, while the accuracies of the needle‐axis and needle‐tip segmentations were quantitatively compared to implementations of two other needle segmentation algorithms within the literature. Results: The needle segmentation algorithm demonstrated a > 99 % accuracy in identifying the TRUS image at the moment of needle insertion from the collection of real‐time TRUS images throughout the insertion and withdrawal of the biopsy needle. The segmented biopsy‐needle axes were accurate to within 2.3 ± 2.0 ° and 0.48 ± 0.42 mm of the gold standard. Identification of the needle tip to within half of the biopsy‐core length( < 10 mm )was 95% successful with a mean error of 2.4 ± 4.0 mm . Needle‐tip detection using the temporal‐based algorithm was significantly more accurate( p < 0.001 )than the other two algorithms tested, while the segmentation of the needle axis was not significantly different between the three algorithms. Conclusions: The temporal‐based needle segmentation algorithm accurately segments the location of the biopsy core from 2D TRUS images of clinical prostate biopsy procedures. The results for needle‐tip localization demonstrated that the temporal‐based algorithm is significantly more accurate than implementations of some existing needle segmentation algorithms within the literature.