Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT

Um, Hyemin; Jiang, Jue; Thor, Maria; Rimner, Andreas; Luo, Leo Y.; Deasy, Joseph O.; Veeraraghavan, Harini

doi:10.48550/arxiv.2005.13690

Cited by 2 publications

(3 citation statements)

References 11 publications

(12 reference statements)

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“…The incremental multiple resolution residual network (iMRRN) is one of the best performing deep learning methods to have been developed for volumetric lung tumor segmentation from CT images [ 8 , 9 , 10 , 12 ]. The iMRRN extends the full resolution residual neural network by combining features at multiple image resolutions and feature levels.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

2023

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Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset “Lung-PET-CT-Dx”, obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine.

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Section: Introductionmentioning

confidence: 99%

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

2023

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“…Although CNNs exhibit impressive performance when the training and testing data are drawn from the same distribution [46,34,55,19,57], variations specific to on-1 arXiv:2106.11942v1 [cs.CV] 22 Jun 2021 site clinical data may result in decreased performance [21,20,51]. For example [18] found that organ deformations due to an abdominal compression technique impaired the performance of an externally trained CNN model.…”

Section: Introductionmentioning

confidence: 99%

“…8 See existing surveys of deep-learning for radiotherapy for explanations of machine learning, deep learning, and CNNs. 9 Although CNNs exhibit impressive performance when the training and testing data are drawn from the same distribution, 5,[10][11][12][13] variations specific to on-site clinical data may result in decreased performance. [14][15][16] For example, it has been found that organ deformations due to an abdominal compression technique impaired the performance of an externally trained CNN model.…”

Section: Introductionmentioning

confidence: 99%

RootPainter3D: Interactive‐machine‐learning enables rapid and accurate contouring for radiotherapy

et al. 2021

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Purpose: Organ-at-risk contouring is still a bottleneck in radiotherapy,with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. Methods:We implement an open-source interactive-machine-learning software application that facilitates corrective-annotation for deep-learning generated contours on X-ray CT images. A trained-physician contoured 933 hearts using our software by delineating the first image, starting model training, and then correcting the model predictions for all subsequent images. These corrections were added into the training data, which was used for continuously training the assisting model. From the 933 hearts, the same physician also contoured the first 10 and last 10 in Eclipse (Varian) to enable comparison in terms of accuracy and duration. Results: We find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods. After 923 images had been delineated, hearts took 2 min and 2 s to delineate on average, which includes time to evaluate the initial model prediction and assign the needed corrections, compared to 7 min and 1 s when delineating manually. Conclusions: Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computerscientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows.

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Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT

Cited by 2 publications

References 11 publications

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

RootPainter3D: Interactive‐machine‐learning enables rapid and accurate contouring for radiotherapy

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