Evidence map›Paper›PMID 35462613›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Multi-scale organs image segmentation method improved by squeeze-and-attention based on partially supervised learning.

Mao Hongdong, Cao Guogang, Zhang Shu, Liu Shunkun, Kong Deqing, Li Sicheng, Peng Zeyu, Wu Yan, Chen Ying, Dai Cuixia

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Network conditioning for synergistic learning on partial annotations.Proceedings of machine learning research · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Mao HongdongSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Cao GuogangSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China. guogangcao@163.com.
Zhang ShuSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Liu ShunkunSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Kong DeqingSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Li SichengSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Peng ZeyuSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Wu YanSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Chen YingSchool of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China.
Dai CuixiaSchool of Science, Shanghai Institute of Technology, Shanghai, China.
Shanghai Institute of Technology · CN

Funding

National Natural Science Foundation of China 61675134National Natural Science Foundation of China 61976140National Natural Science Foundation of China 62175156National Natural Science Foundation of China 81827807Shanghai Committee of Science and Technology 19441905800Wenzhou Medical University Key Laboratory Open Project K181002
6 · The paper itself

Abstract

purposeRadiotherapy is one of most treatments for tumors. To accurately control the radiation dose distribution and lessen the radiation damage to normal tissues and organs in radiotherapy, it is essential to delineate organs at risk (OARs) precisely. However, manual delineating and some traditional methods are labor-intensive and time-consuming. There is an urgent need for fast and precise segmentation methods in radiotherapy.

methodsThis paper proposes a fully automatic segmentation method based on the 3D U-Net for multi-organ in head and neck. It introduces squeeze-and-attention blocks to gather multi-scale context information and the receptive field block to balance the performance between large-sized and small-sized organs. Furthermore, it is trained by the marginal and exclusion loss function in a partially supervised learning mode.

resultsWe evaluated the model with dice similarity coefficient (DSC), 95% Hausdorff distance (95HD) and inference time. Its average DSC is 0.829, which is 4.5%, 3.2%, and 2.4% higher than AnatomyNet's, nnU-net's, and FocusNet's, respectively, and its average 95HD is 2.19. Moreover, its inference time and parameters are 63% and 60% less than FocusNetv2's.

conclusionFor the segmentation of OARs in head and neck, our model is more accurate than AnatomyNet, faster than FocusNetv2, and better balances between segmentation accuracy and inference time. It demonstrates that our method is more applicable for clinical treatment.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedHeadHumansOrgans at RiskSupervised Machine LearningImage segmentationMulti-scale organsPartially supervised learningSqueeze-and-attention

Identifiers

PMID35462613
OpenAlexW4224313154

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.