Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks

Cui, Yunhao; Arimura, Hidetaka; Nakano, Risa; Yoshitake, Tsuyoshi; Shioyama, Yoshiyuki; Yabuuchi, Hidetake

doi:10.1093/jrr/rraa132

Cited by 15 publications

(25 citation statements)

References 36 publications

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“…With the same number of cases, our method is more likely to obtain better segmentation performance. Compared with the research of Cui Y et al, 25 our segmentation results are average. The dense connections and V-net used in their segmentation model provide new ideas for our follow-up research.…”

Section: Discussionmentioning

confidence: 72%

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Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet

Jin

Luo

et al. 2022

Technol Cancer Res Treat

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Introduction: Radiotherapy is one of the most effective ways to treat lung cancer. Accurately delineating the gross target volume is a key step in the radiotherapy process. In current clinical practice, the target area is still delineated manually by radiologists, which is time-consuming and laborious. However, these problems can be better solved by deep learning-assisted automatic segmentation methods. Methods: In this paper, a 3D CNN model named 3D ResSE-Unet is proposed for gross tumor volume segmentation for stage III NSCLC radiotherapy. This model is based on 3D Unet and combines residual connection and channel attention mechanisms. Three-dimensional convolution operation and encoding-decoding structure are used to mine three-dimensional spatial information of tumors from computed tomography data. Inspired by ResNet and SE-Net, residual connection and channel attention mechanisms are used to improve segmentation performance. A total of 214 patients with stage III NSCLC were collected selectively and 148 cases were randomly selected as the training set, 30 cases as the validation set, and 36 cases as the testing set. The segmentation performance of models was evaluated by the testing set. In addition, the segmentation results of different depths of 3D Unet were analyzed. And the performance of 3D ResSE-Unet was compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet. Results: Compared with other depths, 3D Unet with four downsampling depths is more suitable for our work. Compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet, 3D ResSE-Unet can obtain superior results. Its dice similarity coefficient, 95th-percentile of Hausdorff distance, and average surface distance can reach 0.7367, 21.39mm, 4.962mm, respectively. And the average time cost of 3D ResSE-Unet to segment a patient is only about 10s. Conclusion: The method proposed in this study provides a new tool for GTV auto-segmentation and may be useful for lung cancer radiotherapy.

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Section: Discussionmentioning

confidence: 72%

“… 24 To achieve the delineation of GTV for LC stereotactic body radiation therapy, Cui Y et al proposed CT-based dense V-networks with a DSC of 0.82. 25 Based on the above research, we reason that the automatic segmentation of GTV for LC radiotherapy can be achieved through CNNs. However, the above studies have three issues.…”

Section: Introductionmentioning

confidence: 99%

Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet

Jin

Luo

et al. 2022

Technol Cancer Res Treat

View full text Add to dashboard Cite

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“…The study by Cui et al . [ 40 ] used DVNs network to automatically segment lung tumors with DSC of 83.2% and HD95 of 4.57 mm; hence the index of HD95 was superior to our study. One possible reason lies in the differences of the CT thickness between our studies.…”

Section: Discussionmentioning

confidence: 77%

“…All patients in our study were treated with IMRT and the layer thickness of their CT was 5 mm, whereas Cui et al . [ 40 ] studied non-small cell patients treated with SBRT and the layer thickness of their scanned CT was 2 mm or 3.3 mm. Besides, lung cancer patients treated with SBRT had smaller tumors (in the Cui et al .…”

Section: Discussionmentioning

confidence: 99%

Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network

Xie

Chen²,

Deng³

et al. 2022

J Transl Med

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Objective This paper intends to propose a method of using TransResSEUnet2.5D network for accurate automatic segmentation of the Gross Target Volume (GTV) in Radiotherapy for lung cancer. Methods A total of 11,370 computed tomograms (CT), deriving from 137 cases, of lung cancer patients under radiotherapy developed by radiotherapists were used as the training set; 1642 CT images in 20 cases were used as the validation set, and 1685 CT images in 20 cases were used as the test set. The proposed network was tuned and trained to obtain the best segmentation model and its performance was measured by the Dice Similarity Coefficient (DSC) and with 95% Hausdorff distance (HD95). Lastly, as to demonstrate the accuracy of the automatic segmentation of the network proposed in this study, all possible mirrors of the input images were put into Unet2D, Unet2.5D, Unet3D, ResSEUnet3D, ResSEUnet2.5D, and TransResUnet2.5D, and their respective segmentation performances were compared and assessed. Results The segmentation results of the test set showed that TransResSEUnet2.5D performed the best in the DSC (84.08 ± 0.04) %, HD95 (8.11 ± 3.43) mm and time (6.50 ± 1.31) s metrics compared to the other three networks. Conclusions The TransResSEUnet 2.5D proposed in this study can automatically segment the GTV of radiotherapy for lung cancer patients with more accuracy.

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“…Figure 2 l presents the detailed architecture of the Dense V-Network. Cui et al [ 84 ] proposed Dense V-Networks for automatic segmentation of gross tumor volumes (GTVs) in 3D planning CT images for lung cancer patients who underwent stereotactic body radiotherapy (SBRT).…”

Section: Overview Of Deep Learning In Precision Oncologymentioning

confidence: 99%

A Survey on Deep Learning for Precision Oncology

2022

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Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.

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Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks

Cited by 15 publications

References 36 publications

Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet

Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet

Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network

A Survey on Deep Learning for Precision Oncology

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