FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images

Abstract

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods.

Accepted by Medical Image Analysis

References in corpus (13)

• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• Fully Convolutional Networks for Semantic Segmentation
• Squeeze-and-Excitation Networks
• Focal Loss for Dense Object Detection
• Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
• AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
• 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
• Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
• Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Cited by in corpus (4)

• CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
• A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
• UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
• Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation