From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays

Oliveira, Hugo; Mota, Virgínia Fernandes; Machado, Alexei Manso Corrêa; Santos, Jefersson A. dos

doi:10.1016/j.patrec.2020.09.021

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…1) Medical Imaging Datasets: We use a total of ten medical datasets in our experiments (Figure 3). Of these datasets, six are Chest X-Ray datasets (CRX): JSRT [26] with labels for lungs, heart and clavicles; the Montgomery/Shenzhen sets [14], an annotated subset of Chest X-Ray 8 [37] by Tang et al [32] referred to as NIH-labeled, OpenIST 3 with labels for lung segmentation, and the LIDC-IDRI-DRR dataset [21], with generated ribs annotations. We include two Mammographic X-Ray (MRX) image sets, namely INbreast [19] and MIAS [30], with labels for breast region and pectoral muscle segmentation.…”

Section: A Datasetsmentioning

confidence: 99%

Weakly Supervised Few-Shot Segmentation Via Meta-Learning

Gama¹,

Oliveira²,

Marcato³

et al. 2021

Preprint

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Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are very distinct fields of knowledge and usually subject to data scarcity. The results demonstrated the potential of our method, achieving suitable results for segmenting both coffee/orange crops and anatomical parts of the human body in comparison with full dense annotation.

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Section: A Datasetsmentioning

confidence: 99%

Weakly Supervised Few-Shot Segmentation Via Meta-Learning

Gama¹,

Oliveira²,

Marcato³

et al. 2021

Preprint

Self Cite

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“…Image Type # of Images Classes JSRT [16] X-rays 247 Lungs, Clavicles and Hearts Montgomery [7] X-rays 138 Lungs Shenzhen [7] X-rays 662 Lungs NIH-labeled [22] X-rays 100 Lungs OpenIST [23] X-rays 225 Lungs LIDC-IDRI-DRR [11] CT-scans 835 Ribs…”

Section: Datasetmentioning

confidence: 99%

Learning to Segment Medical Images from Few-Shot Sparse Labels

Gama¹,

Oliveira²,

Santos³

2021

Preprint

Self Cite

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In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios. I. INTRODUCTIONMedical images are useful tools to assist doctors in multiple clinical scenarios and to plan for surgery. X-Ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and other imaging modalities are non-invasive methods that can help in diagnosis, pathology localization, anatomical studies, and other tasks [15].Convolutional Neural Networks (CNNs) and their variants are the state of the art for object classification, detection, semantic segmentation and other Computer Vision problems. Classical convolutional networks are known for their large data requirements, often hindering their usage in scenarios were data availability is limited, as in medical imaging. Relatively few medical datasets are publicly available due to privacy and ethical concerns [1,7,10,17,18,20,26].Even among the public datasets, properly curated labeled data is limited due to the need for specialized annotators (i.e. radiologists), severely hampering the creation of general models for medical image understanding. While many datasets contain image-level annotations indicating the presence or absence of a set of medical conditions, the creation of pixel-level labels that allow for the training of semantic segmentation models is much more laborious. Volumetric image modalities as MRIs or CT scans further compound these difficulties by requiring per-slice annotations, often followed by cross-axes analysis to detect inconsistencies, which

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“…A number of works (Chen et al, 2018a;Zhang et al, 2018;Oliveira and dos Santos, 2018) addressed unsupervised DA using CycleGAN-based models to transform source images to resemble those from the target domain. For example, Zhang et al (2018) (Oliveira et al, 2020a) and to transform adult CXR to pediatric CXR for pneumonia classification (Tang et al, 2019c). Unlike most of the studies which utilized DA methods in unsupervised setting, a few studies considered supervised and semi-supervised approaches to adapt to the target domain.…”

Section: Domain Adaptationmentioning

confidence: 99%

Deep Learning for Chest X-ray Analysis: A Survey

Sogancioglu,

Çallı,

van Ginneken

et al. 2021

Preprint

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Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

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From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays

Cited by 17 publications

References 16 publications

Weakly Supervised Few-Shot Segmentation Via Meta-Learning

Weakly Supervised Few-Shot Segmentation Via Meta-Learning

Learning to Segment Medical Images from Few-Shot Sparse Labels

Deep Learning for Chest X-ray Analysis: A Survey

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