DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search

@inproceedings{Guo2021DeepStationingTL,
  title={DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search},
  author={Dazhou Guo and Xianghua Ye and Jia Ge and Xing Di and Le Lu and Lingyun Huang and Guo Tong Xie and Jing Xiao and Zhongjie Liu and Ling Peng and Senxiang Yan and Dakai Jin},
  booktitle={MICCAI},
  year={2021}
}
  • Dazhou Guo, Xianghua Ye, +9 authors Dakai Jin
  • Published in MICCAI 20 September 2021
  • Engineering, Computer Science
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without the voxel-level supervision, the performance is severely limited. LNS is highly context-dependent—LNS boundaries are constrained by anatomical… Expand

Figures and Tables from this paper

SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings
  • Fengze Liu, Ke Yan, +8 authors Dakai Jin
  • Engineering, Computer Science
  • MICCAI
  • 2021
TLDR
This work introduces a fast and accurate method for unsupervised 3D medical image registration, named SAM-enhanced registration (SAME), which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Expand

References

SHOWING 1-10 OF 23 REFERENCES
Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk
TLDR
It is shown that a simple 3D progressive holistically nested network (PHNN), which avoids computationally heavy decoding paths while still aggregating features at different levels of context, can outperform more complicated networks. Expand
Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion
TLDR
Extensive 5-fold cross-validation on 110 esophageal cancer patients demonstrates that both the proposed two-stream chained segmentation pipeline and the PSNN model can significantly improve the quantitative performance over the previous state-of-the-art work by 11% in absolute Dice score. Expand
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search
  • Dazhou Guo, D. Jin, +7 authors Le Lu
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
TLDR
Extensive 4-fold cross-validation on 142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art. Expand
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
TLDR
This work proposes an effective distance-based gating approach to simulate and simplify the high-level reasoning protocols conducted by radiation oncologists, in a divide-and-conquer manner, and results validate significant improvements on the mean recall of GTVLN. Expand
Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest.
TLDR
Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Expand
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
TLDR
A unified LN appearance and inter-LN relationship learning framework to detect the true GTV$_{LN}$ and significantly improves over the state-of-the-art (SOTA) LN classification method. Expand
DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy
TLDR
This work is the first to provide a comprehensive solution for the esophageal GTV and CTV segmentation in radiotherapy planning and proposes a simple yet effective progressive semantically-nested network (PSNN) backbone that outperforms more complicated models. Expand
Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models
TLDR
A method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery is presented. Expand
Clinically applicable deep learning framework for organs at risk delineation in CT images
TLDR
A new deep learning-based method for delineating organs in the area of head and neck performs faster and more accurately than human experts, significantly outperforming human experts and the previous state-of-the-art method. Expand
Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes
TLDR
A method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastsinal, aortic, and N1 nodes is presented. Expand
...
1
2
3
...