CLiP, catheter and line position dataset

Tang, Jennifer S. N.; Seah, Jarrel; Zia, Adil; Gajera, Jay; Schlegel, R.; Wong, Aaron J. N.; Gai, Dayu; Su, Shu; Bose, Tony; Kok, Marcus L.; Jarema, Alex; Harisis, George; Cheng, Chris-Tin; Kavnoudias, Helen; Wang, Wayland; Stein, Anouk; Shih, George; Gaillard, Frank; Dixon, Andrew; Law, Meng

doi:10.1038/s41597-021-01066-8

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…To assess whether models can generalize across clinical distributions, we chose a wide variety of downstream CXR datasets that we used to finetune and validate our models. These datasets came from diverse sites, including Brazil 23 , China 24,25 , United States ,25,,26,27,28,29,30,31 , Spain 32 , Japan 33 , Vietnam 34 and other countries in Eastern Europe and Central Asia. 35 They also addressed a large set of tasks, such as lung nodule detection, line and tube placement, edema severity classification, pediatric pneumonia detection, pneumothorax detection and multi-class differential diagnosis; some datasets address wide-ranging detection tasks for multiple pathologies.…”

Section: Resultsmentioning

confidence: 99%

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

Gulati

Banerjee

et al. 2022

Preprint

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Chest X-rays (CXRs) are a rich source of information for physicians, essential for disease diagnosis and treatment selection. Recent deep learning models aim to alleviate strain on medical resources and improve patient care by automating the detection of diseases from CXRs. However, shortages of labeled CXRs can pose a serious challenge when training models. Currently, models are generally pretrained on ImageNet, but they often need to then be finetuned on hundreds of thousands of labeled CXRs to achieve high performance. Therefore, the current approach to model development is not viable on tasks with only a small amount of labeled data. An emerging method for reducing reliance on large amounts of labeled data is self-supervised learning (SSL), which uses unlabeled CXR datasets to automatically learn features that can be leveraged for downstream interpretation tasks. In this work, we investigated whether self-supervised pretraining methods could outperform traditional ImageNet pretraining for chest X-ray interpretation. We found that SSL-pretrained models outperformed ImageNet-pretrained models on thirteen different datasets representing high diversity in geographies, clinical settings, and prediction tasks. We thus show that SSL on unlabeled CXR data is a promising pretraining approach for a wide variety of CXR interpretation tasks, enabling a shift away from costly labeled datasets.

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Section: Resultsmentioning

confidence: 99%

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

Gulati

Banerjee

et al. 2022

Preprint

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“…In this study, a subset of the open-source dataset, namely CLiP, catheter, and line position dataset [96], was utilized. This dataset is alternatively available as a public catheter and line position challenge dataset.…”

Section: Datasetmentioning

confidence: 99%

Segmentation of catheter tubes and lines in chest x-rays using deep learning models

Kasturi,

Vosoughi,

Hadjiyski

et al. 2024

Medical Imaging 2024: Clinical and Biomedical Imaging

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Catheter tubes and lines are one of the most common abnormal findings on a chest X-ray. Misplaced catheters can cause serious complications, such as pneumothorax, cardiac perforation, or thrombosis, and for this reason, assessment of catheter position is of utmost importance. In order to prevent these problems, radiologists usually examine chest X-rays to evaluate their positions after insertion and throughout intensive care. However, this process is both time-consuming and prone to human error. Efficient and dependable automated interpretations have the potential to lower the expenses of surgical procedures, lessen the burden on radiologists, and enhance the level of care for patients. To address this challenge, we have investigated the task of accurate segmentation of catheter tubes and lines in chest X-rays using deep learning models. In this work, we have utilized transfer learning and transformer-based networks where we utilized two different models: a U-Net++-based model with ImageNet pre-training and an efficientnet encoder, which leverages diverse visual features in ImageNet to improve segmentation accuracy, and a transformer-based U-Net architecture due to its capability to handle long-range dependencies in complex medical image segmentation tasks. Our experiments reveal the effectiveness of the U-Net++-based model in handling noisy and artifact-laden images and TransUNET's potential for capturing

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“…We compare our PAX-Ray++ dataset against other datasets within and outside the CXR domain in Figure 1 c / d . This includes ChestXDet [49], a dataset with mask annotations for lung diseases, CLiP [50], a dataset with annotations for catheters and lines, our PAX-Ray, as well as the popular natural image datasets PASCAL VOC [51] and MSCOCO [52]. PASCAL VOC and MSCOCO are benchmark datasets that support the development of a wide range of computer vision algorithms.…”

Section: Properties Of Pax-ray++mentioning

confidence: 99%

Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling

Seibold,

Jaus,

Fink

et al. 2024

Preprint

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Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatom- ical structures in CXR using pseudo-labeling of three-dimensional computed tomography (CT) scans. Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157 labels and applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. These labels were projected onto a two-dimensional plane, similar to the CXR, allowing the training of detailed semantic segmentation models for CXR without any manual annotation effort. Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0.93 and 0.85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0.95 and 0.83 mIoU. Our anatom- ical segmentations allowed for the accurate extraction of relevant explainable medical features such as the cardio-thoracic-ratio. Conclusion: Our method of volumetric pseudo-labeling paired with CT projection offers a promising approach for detailed anatomical segmentation of CXR with a high agreement with human annotators. This technique may have important clinical implications, particularly in the analysis of various thoracic pathologies.

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CLiP, catheter and line position dataset

Cited by 15 publications

References 24 publications

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

Segmentation of catheter tubes and lines in chest x-rays using deep learning models

Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling

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