  Purpose: To develop a deep network architecture that would achieve fully
automated radiologist-level segmentation of cancers at breast MRI. Materials
and Methods: In this retrospective study, 38229 examinations (composed of 64063
individual breast scans from 14475 patients) were performed in female patients
(age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who
presented between 2002 and 2014 at a single clinical site. A total of 2555
breast cancers were selected that had been segmented on two-dimensional (2D)
images by radiologists, as well as 60108 benign breasts that served as examples
of noncancerous tissue; all these were used for model training. For testing, an
additional 250 breast cancers were segmented independently on 2D images by four
radiologists. Authors selected among several three-dimensional (3D) deep
convolutional neural network architectures, input modalities, and harmonization
methods. The outcome measure was the Dice score for 2D segmentation, which was
compared between the network and radiologists by using the Wilcoxon signed rank
test and the two one-sided test procedure. Results: The highest-performing
network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI
as input and with intensity normalized for each examination. In the test set,
the median Dice score of this network was 0.77 (interquartile range, 0.26). The
performance of the network was equivalent to that of the radiologists (two
one-sided test procedures with radiologist performance of 0.69-0.84 as
equivalence bounds, P <= .001 for both; n = 250). Conclusion: When trained on a
sufficiently large dataset, the developed 3D U-Net performed as well as
fellowship-trained radiologists in detailed 2D segmentation of breast cancers
at routine clinical MRI.
