Dense Contour-Imbalance Aware framework for Colon Gland Instance Segmentation

Abstract

Colon gland images contain rich information about the patient’s condition and the automated instance segmentation results have been widely used to relieve the workload of pathologists and further assist pathologists in making clinical decisions. Previous methods utilize several deep layers’ features and auxiliary contour prediction results to model this task. However, they always fail to capture the complex representation of gland images and bring the contour imbalance problem, resulting in limited performance. To overcome these problems, in this paper, we propose a dense contour-imbalance aware (DCIA) framework by leveraging the recent advances in dense convolutional neural network (DenseNet) and focal loss (FL). First, we integrate all the semantic features generated by DenseNet to explore the “optimal” representation of input image. On this basis, to mitigate the contour imbalance problem in the training stage, we replace common cross-entropy objective with FL. Also, two post-processing techniques, i.e., morphology and convolutional conditional random fields (ConvCRFs) are adopted to further refine the predicted confidence maps, aiming to achieve better performance. Experimental results demonstrate that the proposal outperforms other popular methods on visual perception. Also, compared with the recent popular method DCAN, our framework has higher scores on the testing sets B in terms of three quantitative metrics Object F1, Object Dice, and Object Hausdorff with the improvement of 5.54%, 1.37% and 12.16%, respectively.