Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy Article Swipe

MAKE IMPACT

Zhuotun Zhu , Dakai Jin , Ke Yan , Tsung‐Ying Ho , Xianghua Ye , Dazhou Guo , Chun-Hung Chao , Jing Xiao , Alan Yuille , Le Lü · YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2008.11870

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN). Determining and delineating the spread of GTVLN is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose 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. GTVLN is divided into two subgroups of tumor-proximal and tumor-distal, respectively, by means of binary or soft distance gating. This is motivated by the observation that each category can have distinct though overlapping distributions of appearance, size and other LN characteristics. A novel multi-branch detection-by-segmentation network is trained with each branch specializing on learning one GTVLN category features, and outputs from multi-branch are fused in inference. The proposed method is evaluated on an in-house dataset of $141$ esophageal cancer patients with both PET and CT imaging modalities. Our results validate significant improvements on the mean recall from $72.5\%$ to $78.2\%$, as compared to previous state-of-the-art work. The highest achieved GTVLN recall of $82.5\%$ at $20\%$ precision is clinically relevant and valuable since human observers tend to have low sensitivity (around $80\%$ for the most experienced radiation oncologists, as reported by literature).

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2008.11870

PDF

https://arxiv.org/pdf/2008.11870

OA Status

green

Cited By

3

References

24

Related Works

10

OpenAlex ID

https://openalex.org/W3080091890

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W3080091890

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2008.11870

Digital Object Identifier

Title

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2020

Year of publication

Publication date

2020-08-27

Full publication date if available

Authors

Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung‐Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lü

List of authors in order

Landing page

https://arxiv.org/abs/2008.11870

Publisher landing page

PDF URL

https://arxiv.org/pdf/2008.11870

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2008.11870

Direct OA link when available

Concepts

Segmentation, Radiation therapy, Computer science, Lymph node, Artificial intelligence, Modality (human–computer interaction), Esophageal cancer, Medicine, Radiology, Medical imaging, Cancer, Pattern recognition (psychology), Pathology, Internal medicine

Top concepts (fields/topics) attached by OpenAlex

Cited by

3

Total citation count in OpenAlex

Citations by year (recent)

2022: 2, 2020: 1

Per-year citation counts (last 5 years)

References (count)

24

Number of works referenced by this work

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload