Automatic segmentation of organs‐at‐risks of nasopharynx cancer and lung cancer by cross‐layer attention fusion network with TELD‐Loss

2021 ◽  
Author(s):  
Zuhao Liu ◽  
Chao Sun ◽  
Huan Wang ◽  
Zhiqi Li ◽  
Yibo Gao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Automatic Segmentation ◽  
Cross Layer ◽  
Nasopharynx Cancer

scholarly journals Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

2020 ◽  
Vol 19 ◽  
pp. 153303382094748
Author(s):  
Fuli Zhang ◽  
Qiusheng Wang ◽  
Haipeng Li
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Automatic Segmentation ◽  
Target Volume ◽  
Small Cell ◽  
Small Cell Lung ◽  
Computed Tomography Images ◽  
Positive Rate

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

scholarly journals Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245364
Author(s):  
Robert Neil Finnegan ◽  
Lucia Orlandini ◽  
Xiongfei Liao ◽  
Jun Yin ◽  
Jinyi Lang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Automatic Segmentation ◽  
Maximum Intensity ◽  
Dice Similarity Coefficient ◽  
Observer Variability ◽  
Clinical Practices ◽  
4D Ct ◽  
Lung Cancer Patients ◽  
Respiratory Phases

Incidental radiation exposure to the heart during lung cancer radiotherapy is associated with radiation-induced heart disease and increased rates of mortality. By considering the respiratory-induced motion of the heart it is possible to create a radiotherapy plan that results in a lower overall cardiac dose. This approach is challenging using current clinical practices: manual contouring of the heart is time consuming, and subject to inter- and intra-observer variability. In this work, we investigate the feasibility of our previously developed, atlas-based, automatic heart segmentation tool to delineate the heart in four-dimensional x-ray computed tomography (4D-CT) images. We used a dataset comprising 19 patients receiving radiotherapy for lung cancer, with 4D-CT imaging acquired at 10 respiratory phases and with a maximum intensity projection image generated from these. For each patient, one of four experienced radiation oncologists contoured the heart on each respiratory phase image and the maximum intensity image. Automatic segmentation of the heart on these same patient image sets was achieved using a leave-one-out approach, where for each patient the remaining 18 were used as an atlas set. The consistency of the automatic segmentation relative to manual contouring was evaluated using the Dice similarity coefficient (DSC) and mean absolute surface-to-surface distance (MASD). The DSC and MASD are comparable to inter-observer variability in clinically acceptable whole heart delineations (average DSC > 0.93 and average MASD < 2.0 mm in all the respiratory phases). The comparison between automatic and manual delineations on the maximum intensity images produced an overall mean DSC of 0.929 and a mean MASD of 2.07 mm. The automatic, atlas-based segmentation tool produces clinically consistent and robust heart delineations and is easy to implement in the routine care of lung cancer patients.

scholarly journals Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Xi Liu ◽  
Kai-Wen Li ◽  
Ruijie Yang ◽  
Li-Sheng Geng
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Automatic Segmentation ◽  
Treatment Modalities ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Segmentation Technique ◽  
Radiation Oncologists

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

scholarly journals Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 4644-4652
Author(s):  
Umamaheswari S ◽  
Bakiyalakshmi R ◽  
Stuti Joshi ◽  
Swapneel Chowdhury ◽  
Vibhor Kumar
Keyword(s):  
Lung Cancer ◽  
Automatic Segmentation ◽  
Histogram Equalization ◽  
Training Dataset ◽  
Support Vector ◽  
Medical Procedure ◽  
Liver Malignancy ◽  
Median Filters ◽  
Time Operation ◽  
Image Improvement

Treating malignant growth in the beginning times gives greater treatment choices, less intrusive medical procedure and expands the endurance rate. Finding lung disease just as liver malignancy at a beginning period is a difficult assignment since there are hardly any manifestations right now larger part of the cases are analyzed in later stages. In this paper, an augmented method of segmentation is proposed using the Fuzzy K-means algorithm to observe the initial stage of lung cancer from the human chest X-Ray images. Initially, the investigator used strategies like Histogram Equalization for image improvement, Watershed technique for segmentation and Edge detection for Extraction, etc. Existing Approaches decline to provide certainty in a problem-solving time operation. The datasets are collected from LIDC-IDRI and Kaggle. In this, 100 training dataset input images are taken for training the model: 80 Cancerous images and 20 non-cancerous images. To evaluate the data 20 images are taken. The detection means classifying tumour into three classes (i) non-cancerous tumour, (ii) Less affected tumour (iii) Highly affected tumour. Hence, a new technique to detect lung carcinoma nodules using median filters, Fuzzy K-means clustering for segmentation and SVM (Support Vector Machine) as the classifier is planned on this work. Hence, this way is profitable to the medical instruments producing industries and additionally guide the radiologist for the early analysis of cancer.

scholarly journals Automatic Segmentation of Anatomical Structures from CT Scans of Thorax for RTP

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Emin Emrah Özsavaş ◽  
Ziya Telatar ◽  
Bahar Dirican ◽  
Ömer Sağer ◽  
Murat Beyzadeoğlu
Keyword(s):  
Lung Cancer ◽  
Spinal Canal ◽  
Radiation Treatment ◽  
Automatic Segmentation ◽  
Ct Scans ◽  
Anatomical Structures ◽  
Automated Method ◽  
Symmetric Surface ◽  
Overlap Ratio ◽  
Fuzzy Segmentation

Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP.

scholarly journals Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval

2020 ◽  
Author(s):  
Mario Silva ◽  
Gianluca Milanese ◽  
Stefano Sestini ◽  
Federica Sabia ◽  
Colin Jacobs ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Relative Risk ◽  
Cancer Screening ◽  
Low Dose ◽  
Automatic Segmentation ◽  
Delayed Diagnosis ◽  
Lung Cancer Screening ◽  
Screening Interval ◽  
Nodule Volume ◽  
Automatic Software

Abstract Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). Methods Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. Results In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%. Conclusions Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. Key Points • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis.

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