Improved U-NET network for pulmonary nodules segmentation

Tong, Guofeng; Li, Yong; Chen, Huairong; Zhang, Qingchun; Jiang, Huiying

doi:10.1016/j.ijleo.2018.08.086

Cited by 93 publications

(55 citation statements)

References 9 publications

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“…In summary, in the methods described in i and ii, the training dataset or the features derived from training samples are manipulated to train a learner, whereas, in techniques explained in iii and iv, multiple classifiers (same or different types) are trained using the same training samples. We employed the last approach in which three different U‐Nets were designed and combined for a better PCa prediction, as U‐Net has recently been utilized in the medical image analysis domain for the segmentation tasks and achieved breakthrough results . We integrated those classifiers via a voting‐based system that not only predicts the location of PCa more confidently, but also improves final model specificity through suppressing false‐positive instances.…”

Section: Discussionmentioning

confidence: 99%

“…We employed the last approach in which three different U-Nets were designed and combined for a better PCa prediction, as U-Net has recently been utilized in the medical image analysis domain for the segmentation tasks and achieved breakthrough results. 40,41 We integrated those classifiers via a voting-based system that not only predicts the location of PCa more confidently, but also improves final model specificity through suppressing false-positive instances. Since the majority voting system was employed for the final prediction, the minimum of three networks was required in this study.…”

Section: Discussionmentioning

confidence: 99%

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Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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“…Guofeng et al 26 improved the performance of U-Net for nodule segmentation by including skipped connections within the encoder and decoder paths. The study reports an enhanced performance of U-Net but lacks from 3-D volumetric analysis.…”

Section: Related Workmentioning

confidence: 99%

Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning

Usman

Lee

Byon³

et al. 2020

Sci Rep

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Byung-il Lee 2 & Yeong-Gil Shin 1 Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, enhancing patient survival possibilities. A number of nodule segmentation techniques, which either rely on a radiologist-provided 3-D volume of interest (VOI) or use the constant region of interests (ROIs) for all the slices, are proposed; however, these techniques can only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to freely investigate the nodule presence outside the given VOI and also include the redundant structures (non-nodule) into VOI, which limits the segmentation accuracy. In this work, a novel semi-automated approach for 3-D segmentation of lung nodule in computerized tomography scans, has been proposed. The technique is segregated into two stages. In the first stage, a 2-D ROI containing the nodule is provided as an input to perform a patch-wise exploration along the axial axis using a novel adaptive ROI algorithm. This strategy enables the dynamic selection of the ROI in the surrounding slices to investigate the presence of nodules using a Deep Residual U-Net architecture. This stage provides the initial estimation of the nodule utilized to extract the VOI. In the second stage, the extracted VOI is further explored along the coronal and sagittal axes, in patchwise fashion, with Residual U-Nets. All the estimated masks are then fed into a consensus module to produce a final volumetric segmentation of the nodule. The algorithm is rigorously evaluated on LIDC-IDRI dataset, which is the largest publicly available dataset. The proposed approach achieved the average dice score of 87.5%, which is significantly higher than the existing state-of-the-art techniques. Lung cancer is one of the most severe and highly-prevalent cancers and is the leading cause of cancer deaths worldwide 1. It has been forecasted to be one of the greatest single cause of mortality among the European population in 2019 2. Early diagnosis of lung cancer is crucial to enable possible life-saving interventions 3 , which relies on accurate quantification of pulmonary nodule; albeit pulmonary nodules can be associated with several diseases, their recurrent diagnosis is lung cancer. The continuous monitoring of lung nodule volume is vital to estimate the malignancy and to better forecast, the probability of lung cancer 45. For calculation of volume, the nodule is first segmented, while the manual segmentation of nodule is a tedious and time-consuming task which also introduces the inter and intra-observer variabilities 6. Computer-aided diagnosis (CAD) systems have huge potential to overcome the challenges faced during manual segmentation of pulmonary nodules and can remarkably enhance the productivity of radiologists. Therefore, several automatic nodule segmentation techniques have been proposed to facilitate radiologists, including advanced deep learning and classical image processing based techniques 7. All existing techniques require a 3-D volum...

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“…At present, the segmentation of pulmonary nodules based on neural network has made some progress. 8 Tong et al propose a two-dimensional (2D) U-net to segment pulmonary nodules, which may cause a certain loss to the original three-dimensional (3D) CT images. Reference [9]'s work use transfer learning from a CNN trained on natural images with less annotated data that cannot be a fully dedicated network to segment pulmonary nodules.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of pulmonary nodules in CT images based on 3D‐UNET combined with three‐dimensional conditional random field optimization

Gao

Duan

et al. 2020

Medical Physics

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Purpose: Pulmonary nodules are a potential manifestation of lung cancer. In computer-aided diagnosis (CAD) of lung cancer, it is of great significance to extract the complete boundary of the pulmonary nodules in the computed tomography (CT) scans accurately. It can provide doctors with important information such as tumor size and density, which assist doctors in subsequent diagnosis and treatment. In addition to this, in the molecular subtype and radiomics of lung cancer, segmentation of lung nodules also plays a pivotal role. Existing methods are difficult to use only one model to simultaneously treat the boundaries of multiple types of lung nodules in CT images. Method: In order to solve the problem, this paper proposed a three-dimensional (3D)-UNET network model optimized by a 3D conditional random field (3D-CRF) to segment pulmonary nodules. On the basis of 3D-UNET, the 3D-CRF is used to optimize the sample output of the training set, so as to update the network weights in training process, reduce the model training time, and reduce the loss rate of the model. We selected 936 sets of pulmonary nodule data for the lung image database consortium and image database resource initiative (LIDC-IDRI) 1 database to train and test the model. What's more, we used clinical data from partner hospitals for additional validation. Results and conclusions: The results show that our method is accurate and effective. Particularly, it shows more significance for the optimization of the segmentation of adhesive pulmonary nodules (the juxta-pleural and juxta-vascular nodules) and ground glass pulmonary nodules (GGNs).

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Improved U-NET network for pulmonary nodules segmentation

Cited by 93 publications

References 9 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning

Segmentation of pulmonary nodules in CT images based on 3D‐UNET combined with three‐dimensional conditional random field optimization

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