A precise segmentation of breast tissue is often required for computer-aided diagnosis (CAD) of breast MRI.
Only a few methods have been proposed to automatically segment breast in MRI. Authors reported satisfactory
performance, but a fair comparison has not been done yet as all breast segmentation methods were evaluated on
their own data sets with different manual annotations. Moreover, breast volume overlap measures, which were
commonly used for evaluations, do not seem to be adequate to accurately quantify the segmentation qualities.
Breast volume overlap measures are not sensitive to small errors, such as local misalignments, because the
breast appears to be much larger than other structures. In this work, two atlas-based approaches and a breast
segmentation method based on Hessian sheetness filter are exhaustively evaluated and benchmarked on a data
set of 52 manually annotated breast MR images. Three quantitative measures including dense tissue error,
pectoral muscle error and pectoral surface distance are defined to objectively reflect the practical use of breast
segmentation in CAD methods. The evaluation measures provide important evidence to conclude that the three
evaluated techniques perform accurate breast segmentations. More specifically, the atlas-based methods appear
to be more precise, but require larger computation time than the sheetness-based breast segmentation approach.