Dynamic contrast enhanced Breast MRI (DCE BMRI) has emerged as powerful tool in the diagnostic work-up of breast
cancer. While DCE BMRI is very sensitive, specificity remains to be an issue. Consequently, there is a need for features
that support the classification of enhancing lesions into benign and malignant lesions. Traditional features include the
morphology and the texture of a lesion, as well as the kinetic parameters of the time-intensity curves, i.e., the temporal
change of image intensity at a given location. The kinetic parameters include initial contrast uptake of a lesion and the
type of the kinetic curve. The curve type is usually assigned to one of three classes: persistent enhancement (Type I),
plateau (Type II), and washout (Type III). While these curve types show a correlation with the tumor type (benign or
malignant), only a small sub-volume of the lesion is taken into consideration and the curve type will depend on the
location of the ROI that was used to generate the kinetic curve. Furthermore, it has been shown that the curve type
significantly depends on which MR scanner was used as well as on the scan parameters.
Recently, it was shown that the heterogeneity of a given lesion with respect to spatial variation of the kinetic curve type
is a clinically significant indicator for malignancy of a tumor. In this work we compare four quantitative measures for the
degree of heterogeneity of the signal enhancement ratio in a tumor and evaluate their ability of predicting the dignity of a
tumor. All features are shown to have an area under the ROC curve of between 0.63 and 0.78 (for a single feature).
Our goal was to develop and evaluate a reliable segmentation method to delineate axillary lymph node (ALN) from
surrounding tissues on US images as the first step of building a multi-modality CADx system for staging ALN.
Ultrasound images of 24 ALN from 18 breast cancer patients were used. An elliptical model algorithm was used to fit
ALNs boundaries using the following steps: reduce image noise, extract image edges using the Canny edge detector,
select edge pixels and fit an ellipse by minimizing the quadratic error, Find the best fitting ellipse based on RANSAC.
The segmentation was qualitatively evaluated by 3 expert readers using 4 aspects: Orientation of long axis (OLA):
within +- 45 degrees, or off by +-45 degrees, overlap (OV): the fitted ellipse completely included ALN, partially
included ALN, or missed the ALN, size (SZ): too small, good within 20% error margin, or too large, and aspect ratio
(AR): correct or wrong. Nightly six % of ALNs were correctly evaluated by all readers in terms of OLA and AR, 90.2%
in terms of OV and 86.11 in terms of SZ. Readers agreed that the segmentation was correct in 70% of the cases in all
aspects. Due to small sample size and small variation among readers, we don't have power to show the accuracy of them
Dynamic Contrast Enhanced Breast MR Imaging (DCE BMRI) has emerged as a modality for breast cancer diagnosis. In
this modality a temporal sequence of volume images of the breasts is acquired, where a contrast agent is injected after
acquisition of the first 3D image. Since the introduction of the modality, research has been directed at the development
of computer-aided support for the diagnostic workup. This includes automatic segmentation of mass-like lesions, lesion
characterization, and lesion classification. Robustness, user-independence, and reproducibility of the results of
computerized methods are essential for such methods to be acceptable for clinical application.
A previously proposed and evaluated computerized lesion segmentation method has been further analyzed in this study.
The segmentation method uses as input a subtraction image (post-contrast - pre-contrast) and a user defined region of
interest (ROI). Previous evaluation studies investigated the robustness of the segmentation against variations in the user
selected ROI. Robustness of the method against variations in the image data itself has so far not been investigated. To fill
this gap is the purpose of this study.
In this study, the segmentation algorithm was applied to a series of subtraction images built from the pre-contrast volume
and all available post-contrast image volumes, successively. This provides set of typically 4-5 delineations per lesion,
each based on a different phase of the dynamic sequence.
Analysis of the apparent lesion volumes derived from these delineations and comparison to manual delineations showed
that computerized segmentation is more robust and reproducible than manual segmentation, even if computer
segmentations are computed on subtraction images derived from different dynamic phases of the DCE MRI study, while
all manual segmentations of a lesion are derived from one and the same dynamic phase of the study.
Furthermore, it could be shown that the rate of apparent change of lesion volume over the course of a DCE MRI study is
significantly dependent on the lesion type (benign vs. malignant).
Dynamic contrast enhanced breast MRI (DCE BMRI) is an emerging tool for breast cancer diagnosis. There is
a clear clinical demand for computer-aided diagnosis (CADx) tools to support radiologists in the diagnostic
reading process of DCE BMRI studies. A crucial step in a CADx system is the segmentation of tumors,
which allows for accurate assessment of the 3D lesion size and morphology. In this paper we propose a semiautomatic
segmentation procedure for suspicious breast lesions. The proposed methodology consists of four steps:
(1) Robust seed point selection. This interaction mode ensures robustness of the segmentation result against
variations in seed-point placement. (2) Automatic intensity threshold estimation in the subtraction image.
(3)Connected component analysis based on the estimated threshold. (4) A post-processing step that includes
non-enhancing portions of the lesion into the segmented area and removes attached vessels. The proposed
methodology was applied to DCE BMRI data acquired at different institutions using different protocols.