Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

Purpose Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge-based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. Methods Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning-based method (DLM) using a dilated convolution neural network. A data set of 115 T1-weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI-RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5-75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm(3) x 0.64 mm(3) x 1.00 mm(3). In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. Results According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 +/- 0.003 vs 0.009 +/- 0.011, P < 0.01) and HD95 (2.58 +/- 1.78 mm vs 3.37 +/- 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 +/- 0.009 vs 0.030 +/- 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 +/- 0.006 vs 0.984 +/- 0.008, P = 0.08) or HD (24.14 +/- 20.69 mm vs 12.81 +/- 27.28 mm, P = 0.05). Conclusion Both optimized knowledge-based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust method. A quantitative comparison, however, did not indicate a preference for one method over the other.