The work focuses on a unique medical repository of digital Uterine Cervix images ("Cervigrams") collected
by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multi-year studies. NCI
together with the National Library of Medicine is developing a unique web-based database of the digitized cervix
images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis
of the cervigram content to support the cancer research. In recent works, a multi-stage automated system for
segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The
current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In
accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models
(and corresponding methods) are suggested. The shape models are embedded within an active contour framework
that relies on image features. Experiments indicate that incorporation of the prior shape information augments
previous results.
In this work we focus on the generation of reliable ground truth data for a large medical repository of digital
cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). This work is part of an
ongoing effort conducted by NCI together with the National Library of Medicine (NLM) at the National Institutes
of Health (NIH) to develop a web-based database of the digitized cervix images in order to study the evolution
of lesions related to cervical cancer. As part of this effort, NCI has gathered twenty experts to manually segment
a set of 933 cervigrams into regions of medical and anatomical interest. This process yields a set of images
with multi-expert segmentations. The objectives of the current work are: 1) generate multi-expert ground truth
and assess the diffculty of segmenting an image, 2) analyze observer variability in the multi-expert data, and
3) utilize the multi-expert ground truth to evaluate automatic segmentation algorithms. The work is based on
STAPLE (Simultaneous Truth and Performance Level Estimation), which is a well known method to generate
ground truth segmentation maps from multiple experts' observations. We have analyzed both intra- and inter-expert
variability within the segmentation data. We propose novel measures of "segmentation complexity" by
which we can automatically identify cervigrams that were found difficult to segment by the experts, based on
their inter-observer variability. Finally, the results are used to assess our own automated algorithm for cervix
boundary detection.
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