Paper Reading AI Learner

Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy

2022-06-07 16:03:33
Xiao Liang, Howard Morgan, Ti Bai, Michael Dohopolski, Dan Nguyen, Steve Jiang

Abstract

CBCT-based online adaptive radiotherapy (ART) calls for accurate auto-segmentation models to reduce the time cost for physicians to edit contours, since the patient is immobilized on the treatment table waiting for treatment to start. However, auto-segmentation of CBCT images is a difficult task, majorly due to low image quality and lack of true labels for training a deep learning (DL) model. Meanwhile CBCT auto-segmentation in ART is a unique task compared to other segmentation problems, where manual contours on planning CT (pCT) are available. To make use of this prior knowledge, we propose to combine deformable image registration (DIR) and direct segmentation (DS) on CBCT for head and neck patients. First, we use deformed pCT contours derived from multiple DIR methods between pCT and CBCT as pseudo labels for training. Second, we use deformed pCT contours as bounding box to constrain the region of interest for DS. Meanwhile deformed pCT contours are used as pseudo labels for training, but are generated from different DIR algorithms from bounding box. Third, we fine-tune the model with bounding box on true labels. We found that DS on CBCT trained with pseudo labels and without utilizing any prior knowledge has very poor segmentation performance compared to DIR-only segmentation. However, adding deformed pCT contours as bounding box in the DS network can dramatically improve segmentation performance, comparable to DIR-only segmentation. The DS model with bounding box can be further improved by fine-tuning it with some real labels. Experiments showed that 7 out of 19 structures have at least 0.2 dice similarity coefficient increase compared to DIR-only segmentation. Utilizing deformed pCT contours as pseudo labels for training and as bounding box for shape and location feature extraction in a DS model is a good way to combine DIR and DS.

Abstract (translated)

URL

https://arxiv.org/abs/2206.03413

PDF

https://arxiv.org/pdf/2206.03413.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot