Segmentation of breast MR images remains a challenge and a necessity for a variety of quantitative applications. We present a semi-automatic methodology for segmentation of breast tissue for the special case of low resolution, low flip angle chemical shift encoded MRI (CSE-MRI) with water-fat separation. User interaction is required to set the bounds of the segmentation, while the chest wall and skin are segmented automatically. The results differed with corrections by an experienced radiologist by 4.2% average error per case. The method exhibits comparable accuracy to published methods and high agreement between non-expert reviewers.
Algorithm
Starting with an axial breast MR image, the lateral boundaries of the breast are designated with a published Vcut method1,2 modified to have the user define the Vcut on the most superior slice where the edges of the pectoralis muscle are distinguishable. The superior and inferior boundaries are defined by the user through visual inspection of the volume in the sagittal plane.
The chest wall is identified automatically by an algorithm that traces the gradient magnitude image. The sternum is detected first on the image as it is the most consistently identifiable landmark of the chest wall. From the sternum, a path is traced laterally one pixel at a time, choosing the largest weighted value of the gradient at the leading edge of the trace (Figure 2). Once the path has reached the lateral boundary of the breast, it terminates. Constraining this trace to a low tortuosity prevents it from deviating far from the true chest wall.
Segmentation starts on the central slice and continues to the selected superior and inferior boundaries. The location of the sternum in the current slice is used to define the starting location for sternum identification in the next slice.
An automatic thresholding operation was used to identify the skin-air boundary. The average skin thickness (pixels) is estimated using the inner edges of the skin using the Canny method5. A border of this thickness is then excluded on the entire skin-air boundary. The overall segmentation strategy is summarized in Figure 4.
Validation
This study was IRB-approved and HIPAA compliant. To validate the method, 50 consecutive patients undergoing routine clinical breast MRI underwent informed consent. Patients with a current or previous diagnosis of breast cancer or breast implants were excluded, resulting in 27 subjects.
Images with a resolution of 1.4x1.4x2.5mm (44 slices, 1° flip angle, 1:15 acquisition time) were acquired using a multi-echo chemical shift encoded (CSE) technique and reconstructed to generate fat/water separated images using a previously described CSE-MRI method4 .
All cases were processed automatically by the algorithm after the user defined boundaries. Once segmented, one fellowship-trained breast radiologist with 7 years of experience and two non-clinical researchers each manually corrected the automated segmentations on a slice-by-slice basis.
Inclusion errors were defined as voxels in which the algorithm selected non-breast tissue, and exclusion errors were defined as voxels in which the algorithm failed to select breast tissue, according to the reviewer. Percent total difference was calculated as the sum of these errors divided by the total segmented volume defined by the radiologist. The corrected segmentations of the radiologist were compared to the uncorrected algorithm as well as to each of the lay reviewers.
[1] Nie K, Chen JH, Chan S, Chau MK, Yu HJ, Bahri S, Tseng T, Nalcioglu O, Su MY. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys. 2008 Dec; 35(12):5253-62.
[2] Muqing Lin, Jeon-Hor Chen, Xiaoyong Wang, Siwa Chan, Siping Chen, and Min-Ying Su. Template-based automatic breast segmentation on MRI by excluding the chest region. Medical physics, 40(12):122301, 2013.
[3] Wang, Lei, et al. "Fully automatic breast segmentation in 3D breast MRI." 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2012.
[4] Roberta M Strigel, Leah Henze Bancroft, Diego Hernando, and Scott B Reeder. Proton density water fraction as a measurement of breast fibroglandular tissue volume and concentration. Abstract 2480.Singapore, May 2016. International Society for Magnetic Resonance in Medicine 23rd Annual Meeting & Exhibition.
[5] Canny, John, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, 1986, pp. 679-698.