Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

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Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images

2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) ◽

10.1109/icieam48468.2020.9111950 ◽

2020 ◽

Author(s):

Abdul Qayyum ◽

Chun Kit Ang ◽

S. Sridevi ◽

M. K. A. Ahamed Khan ◽

Lim Wei Hong ◽

...

Keyword(s):

At Risk ◽

Deep Learning ◽

Organs At Risk ◽

Automatic Segmentation ◽

Learning Model ◽

Ct Images ◽

Deep Learning Model

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A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer

Scientific Reports ◽

10.1038/s41598-021-02330-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weijun Chen ◽

Cheng Wang ◽

Wenming Zhan ◽

Yongshi Jia ◽

Fangfang Ruan ◽

...

Keyword(s):

Lung Cancer ◽

At Risk ◽

Rectal Cancer ◽

Deep Learning ◽

Organs At Risk ◽

Similarity Index ◽

Ct Images ◽

Target Area ◽

Ct Image ◽

Significant Difference

AbstractRadiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (Dv) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in Dv between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification.

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Preliminary Clinical Study of the Differences Between Interobserver Evaluation and Deep Convolutional Neural Network-Based Segmentation of Multiple Organs at Risk in CT Images of Lung Cancer

Frontiers in Oncology ◽

10.3389/fonc.2019.00627 ◽

2019 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Jinhan Zhu ◽

Yimei Liu ◽

Jun Zhang ◽

Yixuan Wang ◽

Lixin Chen

Keyword(s):

Neural Network ◽

Lung Cancer ◽

At Risk ◽

Clinical Study ◽

Convolutional Neural Network ◽

Organs At Risk ◽

Ct Images ◽

Deep Convolutional Neural Network ◽

Multiple Organs

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Effects of CT images with different reconstruction energy on radiotherapy planning for patients with non-small cell lung cancer

10.21203/rs.3.rs-51290/v1 ◽

2020 ◽

Author(s):

Tian Xiufang ◽

Liu Kun ◽

Wang Jing ◽

Zhang Jiandong ◽

Yong Hou

Keyword(s):

Lung Cancer ◽

At Risk ◽

Small Cell Lung Cancer ◽

Organs At Risk ◽

Ct Images ◽

Small Cell ◽

Radiotherapy Planning ◽

Target Area ◽

Small Cell Lung ◽

Ct Value

Abstract BACKGROUND With the development of the CT technology, multi-energy technology is applied to CT imaging, which improves the time resolution, spatial resolution and density resolution of CT system. It enables the CT imaging system to achieve clearer image display under safe and low-dose conditions, so that the disease can be displayed more quickly, and the disease can be identified more early and more clearly. METHODS 38 non-small cell lung cancer patients were selected for energy spectral scanning. All energy spectral images obtained were transferred to the DiscoverTM CT post-processing workstation to generate 40 keV, 60 keV, 80 keV, 100 keV, 120 keV, and 140 keV single-energy images. Then the single-energy images were imported to Eclipse, and the oncologist contours the target area and organs at risk (OARs) on the single-energy images described above, and then the physicist designed radiotherapy plans to perform statistical analysis on the tissue CT value and target volume of each single-energy image, and to compare dosimetry of different plans about the organs at risk and the target area. RESULTS The CT values of GTV, heart, lung, and spinal cord of different energy CT images are statistically different (P < 0.05). Among them, the CT value of each tissue obtained in the 40 keV group is the largest, and the CT value decreases with the increase of energy. There were no statistically significant differences in the homogeneity index(HI), the conformity index(CI), the maximum dose, the minimum dose and the average dose of the gross tumor volume (GTV) delineated on CT images of different energy (P > 0.05), as well as the organs at risk. CONCLUSIONS When CT images of different energies obtained from energy spectral CT scans are used in the design of radiotherapy planning, there are no significant differences in target area outlines and doses caused by energy factors, but the differences in tissue CT values have statistical significant.

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Effects of deep inspiration breath hold on prone photon or proton irradiation of breast and regional lymph nodes

Scientific Reports ◽

10.1038/s41598-021-85401-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bruno Speleers ◽

Max Schoepen ◽

Francesca Belosi ◽

Vincent Vakaet ◽

Wilfried De Neve ◽

...

Keyword(s):

Lung Cancer ◽

At Risk ◽

Lymph Node ◽

Organs At Risk ◽

Lymph Node Status ◽

Breath Hold ◽

Deep Inspiration ◽

Proton Radiotherapy ◽

Regional Lymph Nodes ◽

Deep Inspiration Breath Hold

AbstractWe report on a comparative dosimetrical study between deep inspiration breath hold (DIBH) and shallow breathing (SB) in prone crawl position for photon and proton radiotherapy of whole breast (WB) and locoregional lymph node regions, including the internal mammary chain (LN_MI). We investigate the dosimetrical effects of DIBH in prone crawl position on organs-at-risk for both photon and proton plans. For each modality, we further estimate the effects of lung and heart doses on the mortality risks of different risk profiles of patients. Thirty-one patients with invasive carcinoma of the left breast and pathologically confirmed positive lymph node status were included in this study. DIBH significantly decreased dose to heart for photon and proton radiotherapy. DIBH also decreased lung doses for photons, while increased lung doses were observed using protons because the retracting heart is displaced by low-density lung tissue. For other organs-at-risk, DIBH resulted in significant dose reductions using photons while minor differences in dose deposition between DIBH and SB were observed using protons. In patients with high risks for cardiac and lung cancer mortality, average thirty-year mortality rates from radiotherapy-related cardiac injury and lung cancer were estimated at 3.12% (photon DIBH), 4.03% (photon SB), 1.80% (proton DIBH) and 1.66% (proton SB). The radiation-related mortality risk could not outweigh the ~ 8% disease-specific survival benefit of WB + LN_MI radiotherapy in any of the assessed treatments.

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