Automatic Pulmonary Lobe Segmentation Using Deep Learning

Tang, Hao; Zhang, Chupeng; Xie, Xiaohui

doi:10.1109/isbi.2019.8759468

Cited by 41 publications

(38 citation statements)

References 13 publications

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“…More recently, the application of DL for medical image segmentation, i.e., convolutional neural networks (CNNs), has gained increased momentum [29]. CNNs can be implemented for segmenting the nuclei of cells [30], brain tumors on MRI scans [31], livers and tumors on CTs [32], the different lobes of the lungs [33], cataract surgery instruments [34], and multiple organs in laparoscopic surgery images [35]. All approaches that offer an end-to-end analysis (from raw images to segmented images) to overcome any previous methods' difficulties suffer.…”

Section: Related Workmentioning

confidence: 99%

Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation

et al. 2020

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Accurate screening for septal defects is important for supporting radiologists' interpretative work. Some previous studies have proposed semantic segmentation and object detection approaches to carry out fetal heart detection; unfortunately, the models could not segment different objects of the same class. The semantic segmentation method segregates regions that only contain objects from the same class. In contrast, the fetal heart may contain multiple objects, such as the atria, ventricles, valves, and aorta. Besides, blurry boundaries (shadows) or a lack of consistency in the acquisition ultrasonography can cause wide variations. This study utilizes Mask-RCNN (MRCNN) to handle fetal ultrasonography images and employ it to detect and segment defects in heart walls with multiple objects. To our knowledge, this is the first study involving a medical application for septal defect detection using instance segmentation. The use of MRCNN architecture with ResNet50 as a backbone and a 0.0001 learning rate allows for two times faster training of the model on fetal heart images compared to other object detection methods, such as Faster-RCNN (FRCNN). We demonstrate a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP). As shown in the results, the proposed MRCNN model achieves good performance in multiclass detection of the heart chamber, with 97.59% for the right atrium, 99.67% for the left atrium, 86.17% for the left ventricle, 98.83% for the right ventricle, and 99.97% for the aorta. We also report competitive results for the defect detection of holes in the atria and ventricles via semantic and instance segmentation. The results show that the mAP for MRCNN is about 99.48% and 82% for FRCNN. We suggest that evaluation and prediction with our proposed model provide reliable detection of septal defects, including defects in the atria, ventricles, or both. These results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease.

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Section: Related Workmentioning

confidence: 99%

Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation

et al. 2020

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“…The model was trained and validated on the publicly available subset of the LUNA16 challenge. The subset contains 50 CT volumes manually annotated for lung lobes [9]. Since this is only a small training dataset, the following data augmentation strategies were adopted to increase the training set:…”

Section: Online Supplementmentioning

confidence: 99%

Development and validation of an automated radiomic CT signature for detecting COVID-19

Guiot

Vaidyanathan

Deprez

et al. 2020

Preprint

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Background The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits.Objectives To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. MethodsIn this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liège, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity.Results 1381 patients were included in this study. The average age was 64.4±15.8 and 63.8±14.4 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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“…Deep convolutional neural networks have become a powerful tool for image classification, with particular application to medical images [16,17]. These correspond to regularized versions of the traditional multilayer perceptrons (fully connected forward layers) [18,19].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of deep neural network architectures in the identification of bone fissures

2020

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Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.

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Automatic Pulmonary Lobe Segmentation Using Deep Learning

Cited by 41 publications

References 13 publications

Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation

Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation

Development and validation of an automated radiomic CT signature for detecting COVID-19

Evaluation of deep neural network architectures in the identification of bone fissures

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