Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system

Acta Oncol. 2013 Nov;52(8):1676-81. doi: 10.3109/0284186X.2012.754989. Epub 2013 Jan 22.

Abstract

Purpose: To evaluate in two different settings - clinical practice and education/training - the reliability, time efficiency and the ideal sequence of an atlas-based auto-segmentation system in pelvic delineation of locally advanced rectal cancer.

Methods: Fourteen consecutive patients were selected between October and December 2011. The images of four were used as an atlas and 10 used for validation. Two independent operators participated: a Delineator to contour and a Reviewer to perform an independent check (IC). The CTV, pelvic subsites and organs at risk were contoured in four different sequences. These included A: manual; B: auto-segmentation; C: auto-segmentation + manual revision; and D: manual + auto-segmentation + manual revision. Contouring was performed by the Delineator using the same planning CT. All of them underwent an IC by a Reviewer. The time required for all the contours were recorded and overlapping evaluation was assessed using a Dice coefficient.

Results: In the clinical practice setting there have been 13 minutes time saved between sequences A versus sequences B (from 38 to 25 minutes, p = 0.002), a mean Dice coefficient in favor of sequences A for CTV and all subsites (p = 0.0195). In the educational/training setting there have been 35.2 minutes time saved between sequences C and D 8 (from 73.1 min to 37.9 min, p = 0.002).

Conclusion: The preliminary data suggest that the use of an atlas-based auto-contouring system may help improve efficiencies in contouring in the clinical practice setting and could have a tutorial role in the educational/training setting.

Publication types

  • Validation Study

MeSH terms

  • Algorithms
  • Atlases as Topic*
  • Female
  • Follow-Up Studies
  • Humans
  • Lymph Nodes / diagnostic imaging*
  • Male
  • Medical Illustration
  • Pattern Recognition, Automated*
  • Pelvis / diagnostic imaging*
  • Prognosis
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiotherapy Planning, Computer-Assisted / methods*
  • Rectal Neoplasms / diagnostic imaging*
  • Rectal Neoplasms / radiotherapy
  • Rectum / diagnostic imaging*
  • Retrospective Studies
  • Tomography, X-Ray Computed
  • Tumor Burden