A Simple Generic Method for Effective Boundary Extraction in Medical Image Segmentation

Abstract

Accurate identification of the shape and position of organs and abnormal objects (e.g., tumors) in medical images plays an important role in surgical planning as well as in the diagnosis and prognosis of diseases. However, this is difficult to achieve from two-dimensional medical images as these images present inaccurate and ambiguous organ boundaries. Further, traditional image processing-based boundary detection methods such as the Canny edge detector and Sobel operator exhibit poor boundary detection performance for images with substantial noise. Recently, the use of deep learning has resulted in improvements in semantic segmentation in medical images. In this paper, we propose a generic boundary-aware loss function to facilitate the effective discernment of the boundaries of organs and abnormal objects in medical images. Specifically, the proposed loss function introduces a boundary area and assigns higher weights to the loss of pixels located in the boundary area than to those in the non-boundary areas, thereby promoting effective learning in the boundary area. The results of experiments conducted using public medical datasets comprising colon polyp, skin lesion, and chest X-ray data indicate that the standard loss functions, such as cross-entropy loss and Dice loss, combined with the proposed boundary-aware loss function, achieve comparable or better performance than those without the boundary-aware loss function.