PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation

by   Yutong Xie, et al.

It has been widely recognized that the success of deep learning in image segmentation relies overwhelmingly on a myriad amount of densely annotated training data, which, however, are difficult to obtain due to the tremendous labor and expertise required, particularly for annotating 3D medical images. Although self-supervised learning (SSL) has shown great potential to address this issue, most SSL approaches focus only on image-level global consistency, but ignore the local consistency which plays a pivotal role in capturing structural information for dense prediction tasks such as segmentation. In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Specifically, we use the spatial transformations, which produce different augmented views of the same image, as a prior to deduce the location relation between two views, which is then used to align the feature maps of the same local region but being extracted on two views. Next, we construct a local consistency loss to minimize the voxel-wise discrepancy between the aligned feature maps. Thus, our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information. This ability is conducive to downstream segmentation tasks. We conducted an extensive evaluation on four public computerized tomography (CT) datasets that cover 11 kinds of major human organs and two tumors. The results indicate that using pre-trained PGL model to initialize a downstream network leads to a substantial performance improvement over both random initialization and the initialization with global consistency-based models. Code and pre-trained weights will be made available at:


page 1

page 3

page 11

page 13


Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Vision Transformers (ViT)s have shown great performance in self-supervis...

Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation

Deep learning highly relies on the quantity of annotated data. However, ...

A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

The segmentation of medical images is a fundamental step in automated cl...

Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps

Registration is an important part of many clinical workflows and factual...

Virtual embeddings and self-consistency for self-supervised learning

Self-supervised Learning (SSL) has recently gained much attention due to...

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

Due to the intensive cost of labor and expertise in annotating 3D medica...

Siamese Image Modeling for Self-Supervised Vision Representation Learning

Self-supervised learning (SSL) has delivered superior performance on a v...