Dazhou Guo

Senior Research Scientist
@ PAII Inc.
Email: guo2004131 [at] gmail [dot] com
CV | GoogleScholar | LinkedIn

Brief Bio

Since 2019, I have been working as a senior research scientist at PAII Inc. I am supervised and mentored by Dr. Le Lu , and working closely with amazingly talented colleagues and clinical physicians/oncologists. My current leading projects are focusing on the automated segmentation in cancer radiotherapy planning, including esophageal Gross Tumor Volume (GTV) segmentation, Clinical Target Volume (CTV) segmentation, chest and head & neck Organ At Risk (OAR) parsing, and Lymph Node Station (LNS) & Lymph Node (LN) delineation. Before that, I received my Ph.D. degree in Computer Science from University of South Carolina. I obtained my Master's degree from Tianjin University and Bachelor's degree from Dalian University of Technology

Updates

[06/2021] Two papers on lymph node station parsing and unsupervised deformable registration are accepted by MICCAI 2021.

[05/2021] Three abstracts are accepted by American Society for Radiation Oncology (ASTRO) 2021.

[05/2021] One co-author paper on COVID-19 risk factor study is accepted by Frontiers in Radiology 2021.

[02/2021] Guest Editor for a special issue "Machine Learning for Quantitative Neuroimaging Analysis" in Frontiers in Neuroscience (IF:3.7, h5-index 80). Welcome to contribute!

[09/2020] One paper titled "DeepTarget" is accepted by Medical Image Analysis. (Special Issue of MICCAI-2019 Best Papers)

[11/2020] One paper on esophageal GTV & CTV segmentation is accepted by Medical Image Analysis, Selected Papers Special Issue.

[09/2020] Two abstracts are accepted by RSNA (both oral presentations).

[05/2020] Two papers on the lymph node delineation are accepted to MICCAI 2020 (both early accepts).

[03/2020] One paper on comprehensive head & neck OAR segmentation is accepted by CVPR 2020.

[08/2019] One papers on degraded image semantic segmentation is accepted by IEEE Trans on Image Processing.

[08/2019] One papers on co-saliency detection is accepted by Neurocomputing.

[04/2019] One papers on weakly supervised object detection is accepted by Neurocomputing.

[04/2019] Two papers on GTV & CTV segmentation are accepted by MICCAI 2019 (both early accepts, one oral presentation).

Publications

2021

SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings. Fengze Liu*, Ke Yan*, Adam Harrison, Dazhou Guo, Le Lu, Alan Yuille, Lingyun Huang, Guotong Xie, Jing Xiao, Xianghua Ye, Dakai Jin. (*equal contribution) MICCAI, 2021

DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search. Dazhou Guo*, Xianghua Ye*, Jia Ge, Xing Di, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Zhongjie Lu, Ling Peng, Senxiang Yan, Dakai Jin. (*equal contribution) MICCAI, 2021

CT-based risk factors for mortality of patients with COVID-19 Pneumonia in Wuhan, China: a retrospective study. Xiang Li*, Nannan Li*, Zhen Chen*, Ling Zhang*, Ling Ye*, Dakai Jin, Liangxin Gao, Xinhui Liu, Bolin Lai, Jiawen Yao, Dazhou Guo, Hua Zhang, Le Lu, Jing Xiao, Lingyun Huang, Fen Ai, Xiang Wang. (*equal contribution) Frontiers in Radiology, 2021

Comprehensive Head and Neck Organs at Risk Segmentation using Stratified Learning and Neural Architecture Search, Head and Neck Cancer Track, POSTER VIEWING Q&A Session ASTRO 2021

Anatomy Guided Thoracic Lymph Node Station Delineation in CT using Deep Learning Model, Digital Health Innovation Track, POSTER VIEWING Q&A Session ASTRO 2021

Deep Learning Based Lymph Node Gross Tumor Volume Detection via Distance-guided Gating using CT and 18F-FDG PET in Esophageal Cancer Radiotherapy, Gastrointestinal Cancer Track, POSTER VIEWING Q&A Session ASTRO 2021

2020

DeepTarget: Gross Tumor and Clinical Target Volume Segmentation in Esophageal Cancer Radiotherapy. Dakai Jin*, Dazhou Guo*, Tsung-Ying Ho, Adam P Harrison, Jing Xiao, Chen-kan Tseng, Le Lu. (*equal contribution) Medical Image Analysis, 2020 (MICCAI-2019 selected Papers Special Issue)
paper

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search. Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P Harrison, Chun-Hung Chao, Jing Xiao, Le Lu. CVPR, 2020
paper | poster | video

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network. Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung-Ying Ho, Jinzheng Cai, Adam P Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin. MICCAI, 2020 (Early accept)
paper

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-Based Gating Using 3D CT/PET Imaging in Radiotherapy. Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu. MICCAI, 2020 (Early accept)
paper | poster | video

Weakly supervised easy-to-hard learning for object detection in image sequences. Hongkai Yu, Dazhou Guo, Zhipeng Yan, Lan Fu, Jeff Simmons, Craig P Przybyla, Song Wang. Neurocomputing, 2020
paper

Organs at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search. Tsung-Ying Ho, Dazhou Guo, Dakai Jin, Chien-Yu Lin, Le Lu, Tzu-Chen Yen, Jing Xiao. RSNA, 2020 (Oral presentation)

Automated Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk. Tsung-Ying Ho, Dakai Jin, Dazhou Guo, Chen Kan Tseng, Le Lu, Tzu-Chen Yen, Jing Xiao. RSNA, 2020 (Oral presentation)

2019

Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT Using Two-Stream Chained 3D Deep Network Fusion. Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P Harrison, Jing Xiao, Chen-kan Tseng, Le Lu. MICCAI, 2019 (Early accept and oral presentation)
paper | poster

Deep Esophageal Clinical Target Volume Delineation Using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk. Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P Harrison, Jing Xiao, Chen-kan Tseng, Le Lu. MICCAI, 2019 (Early accept)
paper | poster

An easy-to-hard learning strategy for within-image co-saliency detection. Shaoyue Song, Hongkai Yu, Zhenjiang Miao, Dazhou Guo, Wei Ke,Cong Ma, Song Wang. Neurocomputing, 2019
paper

Degraded Image Semantic Segmentation With Dense-Gram Networks. Dazhou Guo, Yanting Pei, Kang Zheng, Hongkai Yu, Yuhang Lu, Song Wang. IEEE Transactions on Image Processing, 2019
paper | code | data

Small Object Sensitive Segmentation of Urban Street Scene With Spatial Adjacency Between Object Classes. Dazhou Guo, Ligeng Zhu, Yuhang Lu, Hongkai Yu, Song Wang. IEEE Transactions on Image Processing, 2019
paper | code

Earlier

Lesion detection using T1-weighted MRI: A new approach based on functional cortical ROIs. Dazhou Guo, Kang Zheng, Song Wang. ICIP, 2017
paper

Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras. Kang Zheng, Xiaochuan Fan, Yuewei Lin, Hao Guo, Hongkai Yu, Dazhou Guo, Song Wang. ICCV, 2017
paper | data

Automated lesion detection on MRI scans using combined unsupervised and supervised methods. Dazhou Guo, Julius Fridriksson, Paul Fillmore, Christopher Rorden, Hongkai Yu, Kang Zheng, Song Wang. BMC Medical Imaging, 2015
paper | code

Regional white matter damage predicts speech fluency in chronic post-stroke aphasia. Alexandra Basilakos, Paul Fillmore, Christopher Rorden, Dazhou Guo, Leonardo Bonilha, Julius Fridriksson. Frontiers in Human Neuroscience, 2014
paper

Chronic Broca's Aphasia Is Caused by Damage to Broca's and Wernicke's Areas. Julius Fridriksson Paul Fillmore, Dazhou Guo, Christopher Rorden. Cerebral Cortex, 2014
paper

Video-Based Action Detection Using Multiple Wearable Cameras. Kang Zheng, Yuewei Lin, Youjie Zhou, Dhaval Salvi, Xiaochuan Fan, Dazhou Guo, Zibo Meng, Song Wang. ECCV Workshop, 2014
paper

Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia. Julius Fridriksson, Dazhou Guo, Paul Fillmore, Audrey Holland, Christopher Rorden. Brain, 2013
paper