Deep learning networks to segment and detect breast terminal duct lobular units, acini, and adipose tissue: a step toward the automated analysis of lobular involution as a marker for breast cancer risk
Allison M. Onken, Suzanne C. Wetstein, Michael E. Pyle, Josien P. W. Pluim, Stuart J. Schnitt, Gabrielle M. Baker, Laura C. Collins, Rulla M. Tamimi, Mitko Veta, Yujing Jan Heng · TU/e Research Portal · 2019
Background: Terminal duct lobular unit (TDLU) involution is the physiological process whereby Type 2 and 3 lobules revert to Type 1 after child-bearing years. TDLU involution (quantitatively assessed by TDLU count per mm2, TDLU span, and acini count per TDLU) is inversely associated with breast cancer risk. The manual assessment of involution is time-consuming and subjective, making it impractical to perform on large epidemiological studies. Deep learning algorithms such as convolutional neural networks (CNNs) could be utilized for rapid and automated assessment of TDLU involution. We designed two CNNs to segment TDLUs and detect acini as the first step toward large-scale assessment of TDLU involution, and a third CNN to segment adipose tissue. Design: Whole slide images (WSIs; n=50) were obtained from the Nurses’ Health Study Incident Benign Breast Disease Study. For each WSI, TDLUs, acini, and adipose tissue were annotated within a region of interest comprising approximately 10% of the total tissue area. In order to assess involution in histologically normal breast parenchyma only, TDLUs with proliferative or metaplastic changes were excluded from manual evaluation. CNNs were engineered to recognize TDLUs, acini, and adipose tissue using 60% of the WSIs for training, 20% as a test set, and 20% for validation. F1 and Dice scores were calculated as accuracy measures to compare CNN segmentation to manual assessment. Results: Our CNNs detected acini, segmented TDLUs, and segmented adipose tissue with accuracy measures of 0.73, 0.84, and 0.86, respectively. Two primary causes of discordance with manual assessment were identified: 1) complex clustering of TDLUs where our CNN had difficulty predicting TDLU boundaries and 2) acini with proliferative or metaplastic changes which our CNN frequently detected as acini but which were intentionally excluded from manual annotation. Conclusion: We have developed a series of deep learning networks to segment and detect TDLUs, acini, and adipose tissue on WSIs. With accuracy measures of >0.7, our CNNs are sufficiently robust to be integrated into a computational pipeline for automated assessment of the quantitative features of TDLU involution, and will be further refined to address sources of discordance with manual assessment. This is the first step toward the large-scale quantification of TDLU involution which, when applied to patient samples, could be used to better determine the breast cancer risk associated with lobule type and degree of involution.