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 diferent 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 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 were exhaustively evaluated and benchmarked on a data set of 52 manually annotated breast MR images. Three quantitative measures including percentage of missed dense tissue, percentage of missed pectoral muscle and pectoral surface distance were defi ned to objectively reflect the practical use of breast segmentation in CAD methods. The evaluation measures provided important evidence to conclude that the three evaluated techniques performed accurate breast segmentations. More speci cally, the atlas-based methods appeared to be more precise, but required larger computation time than the sheetness-based breast segmentation approach.