PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors

TL;DR

This paper introduces PGD-UNet, a novel segmentation network that uses position-guided deformable convolutions and a new pooling method to accurately segment organs and tumors despite irregular shapes, size variations, and label noise.

Contribution

The paper proposes a position-guided deformable UNet with a new pooling module and a noise-robust loss function for improved medical image segmentation.

Findings

Achieved high segmentation accuracy on two challenging tasks.

Effectively handles irregular shapes and size variations.

Reduces impact of label noise during training.

Abstract

Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors as well as the significant class imbalance between the anatomy of interest (AOI) and the background region. In addition, in most situation tumors and normal organs often overlap in medical images, but current approaches fail to delineate both tumors and organs accurately. To tackle such challenges, we propose a position-guided deformable UNet, namely PGD-UNet, which exploits the spatial deformation capabilities of deformable convolution to deal with the geometric transformation of both organs and tumors. Position information is explicitly encoded into the network to enhance the capabilities of deformation. Meanwhile, we introduce a new pooling module to preserve position information lost in conventional max-pooling operation. Besides, due to unclear boundaries between different structures as well as the subjectivity of annotations, labels are not necessarily accurate for medical image segmentation tasks. It may cause the overfitting of the trained network due to label noise. To address this issue, we formulate a novel loss function to suppress the influence of potential label noise on the training process. Our method was evaluated on two challenging segmentation tasks and achieved very promising segmentation accuracy in both tasks.