Boundary-attention Loss Function in Neural Network for Pathological Lymph Nodes Segmentation based on PET/CT Images

Abstract

Automated Lymph Node (LN) detection and segmentation are essential for cancer staging. Positron emission tomography (PET) and computed tomography (CT) imaging are routinely used to detect pathological LNs in clinical. Yet, it is still a difficult task for LN segmentation owing to its low contrast as well as surrounding soft tissues and the variation in nodal size and shape. Deep convolutional neural networks have been widely employed to segment objects in medical images, which choice cross-entropy as loss function. However, it did not consider the severe class imbalance between pathological LNs and the background. Keeping this in mind, we, firstly, present a novel boundary-aware cross-entropy (BCE) loss function, which could up-weight the boundary voxels of LNs. Moreover, we investigate the behavior of multiple loss functions for LNs segmentation, such as cross-entropy loss (CE), focal loss (FL), and generalized Dice loss (GDL). Lastly, we propose a novel strategy that combines BCE, CE and FL loss function with GDL respectively, which could exploit the class re-balancing properties of the GDL for imbalanced category labels between LNs and background. We find that combination of BCE loss function with GDL could alleviate the problem of imbalance of category labels. Four-fold cross validations have been done on 63 volumes containing 214 malignant lymph nodes shows that the combination of BCE loss function with GDL achieved the sensitivity 90% and 85%, and Dice 75% and 77% on SegNet and DeepLabv3+ architecture respectively.