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Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.

Abstract：

:The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

journal_name

Phys Med Biol

journal_title

Physics in medicine and biology

authors

Deeley MA,Chen A,Datteri R,Noble JH,Cmelak AJ,Donnelly EF,Malcolm AW,Moretti L,Jaboin J,Niermann K,Yang ES,Yu DS,Yei F,Koyama T,Ding GX,Dawant BM

doi

10.1088/0031-9155/56/14/021

subject

Has Abstract

pub_date

2011-07-21 00:00:00

pages

4557-77

issue

eissn

0031-9155

issn

1361-6560

pii

S0031-9155(11)85448-5

journal_volume

pub_type

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Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.