Polar representation-based cell nucleus segmentation in non-small cell lung cancer histopathological images. (September 2021) - Xiao, Wei; Jiang, Yanyun; Yao, Zhigang; Zhou, Xiaoming; Lian, Jian; Zheng, Yuanjie
Abstract: Image segmentation is a major area of interest within the field of medical image analysis and processing. In the last few decades, cell nucleus segmentation in non-small lung cancer histopathological images has spawned considerable critical attention. However, most of the previously presented studies have only been concerned with revealing the representation of contours in Cartesian coordinates and suffer from a lack of clarity in connecting the points into a whole contour. Bearing this in mind, we propose a polar representation-based nucleus segmentation model by leveraging the fully convolutional one-stage object detection. In general, center classification and length regression are simultaneously adapted to yield the contour of the nucleus in a polar coordinate. To evaluate the performance of our approach, the comparing experiments are conducted between the state-of-the-art deep learning-based object detection algorithms and the proposed method on one manually collected dataset. Experimental results demonstrate that our model outperformed the state-of-the-art both in efficiency and effectiveness. Highlights: A polar representation-based deep learning model by training on collected LUND. Both polar centeredness and polar IoU loss introduced in the training process. Experimental results illustrate the effectiveness of the proposed solution over the state-of-the-arts.