DeepAI AI Chat
Log In Sign Up

Contour Transformer Network for One-shot Segmentation of Anatomical Structures

by   Yuhang Lu, et al.

Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.


page 1

page 3

page 4

page 5

page 6

page 7

page 10

page 11


Learning to Segment Anatomical Structures Accurately from One Exemplar

Accurate segmentation of critical anatomical structures is at the core o...

Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations

The scarcity of labeled data often limits the application of supervised ...

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

Learning by imitation is one of the most significant abilities of human ...

Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation

Segmentation of organs of interest in 3D medical images is necessary for...

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

The prowess that makes few-shot learning desirable in medical image anal...

Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph

Landmark localization plays an important role in medical image analysis....

Disentangle, align and fuse for multimodal and zero-shot image segmentation

Magnetic resonance (MR) protocols rely on several sequences to properly ...

Code Repositories