Deep Transfer Learning for Segmentation of Anatomical Structures in Chest Radiographs

Oliveira, Hugo; Santos, Jefersson dos

doi:10.1109/sibgrapi.2018.00033

Cited by 19 publications

(35 citation statements)

References 18 publications

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“…Oliveira et al proposed transfer learning-based semantic segmentation for multiclass chest anatomy segmentation. They used pre-trained networks, such as fully convolutional networks (FCN), U-Net, and SegNet with transfer learning [45]. Islam et al presented an efficient lung segmentation model.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…Since the introduction of Image-to-Image Translation GANs, several works [15], [18]- [21], [47], [48] have used these architectures to perform Domain Adaptation between image domains. In the following paragraphs, when available, we will mainly focus on the experiments of the literature in dense labeling tasks.…”

Section: Related Workmentioning

confidence: 99%

“…CyCADA reports mIoU results of 35.4%, frequency weighted Intersection over Union (fwIoU) of 73.8% and Pixel Accuracy of 83.6% in translations between GTA5→CityScapes. Several works improved on CyCADA by plugging a semantic segmentation DNN on one end of an Unpaired Image-to-Image Translation network [19]- [21], achieving comparable results on Computer Vision datasets.…”

Section: Related Workmentioning

confidence: 99%

“…CoDAGANs allows multiple datasets to be used conjointly in the training procedure, enforcing automatic domainshift compensation. It is important to notice that most of the other modern methods in the visual DA literature [17]- [21] only allow for the pairwise training of one source and one target domain. In contrast to these pairwise methods, CoDAGANs learn to perform supervised inference over a common isomorphic representation built upon samples drawn from marginal distributions of multiple domains, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…We claim the following contributions for the current manuscript: 1) a novel approach to perform effective DA in the context of biomedical image segmentation tasks; 2) a new strategy to perform unpaired UDA and SSDA by using generative adversarial networks without the limits of pairwise training; 3) a complete review about the state of the art in image translation for domain adaptation. We also perform an extensive comparison with pairwise baseline methods in the literature [18], [21], [22] -further described in Sections III and V-D -assessing the improvements and limits of CoDA-GANs for dense labeling DA in radiographs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation

2020

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Digitization techniques for biomedical images yield disparate visual patterns in radiological exams. These pattern differences, which can be viewed as a domain-shift problem, may hamper the use of data-driven approaches for inference over these images, such as Deep Neural Networks. Another noticeable difficulty in this field is the lack of labeled data, even though in many cases there is an abundance of unlabeled data available. Therefore, an important step in improving the generalization capabilities of these methods and mitigate domain-shift effects is to perform unsupervised or semi-supervised adaptation between different domains of biomedical images. In this work, we propose a novel approach for segmentation of biomedical images based on Generative Adversarial Networks. The proposed method, named Conditional Domain Adaptation Generative Adversarial Network (CoDAGAN), merges unsupervised networks with supervised deep semantic segmentation architectures in order to create a semi-supervised method capable of learning from both unlabeled and labeled data, whenever labeling is available. We conducted experiments to compare our method with traditional and state-of-the-art baselines by using several domains, datasets, and segmentation tasks. The proposed method yielded consistently better results than the baselines in scarce labeled data scenarios, achieving Jaccard values greater than 0.9 and good segmentation quality in most tasks. Unsupervised Domain Adaptation results were observed to be close to the Fully Supervised Domain Adaptation used in the traditional procedure of fine-tuning pretrained networks.

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