VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

Nguyen, Hoang; Le, Tung T.; Pham, Hieu H.; Nguyen, Ha Q.

doi:10.48550/arxiv.2107.01327

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…In our model, the conditional function constraining the image generation process is defined as the rib skeleton extracted from the healthy chest X-ray images. Specifically, the extraction is performed with a U-Net-based model 66 trained on VinDr-RibCXR dataset 51 , with ϕ as the network parameter.…”

Section: Chest X-ray Image Generation With Ribs Controlmentioning

confidence: 99%

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Prompt-driven Healthy/Diseased Image Pairs Enabling Pixel-level Chest X-ray Pathology Localization

Suo,

Dong,

Cheng

et al. 2024

Preprint

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Medical artificial intelligence (AI) offers great potential for automatic pathology interpretation, but the performance is far behind providing a practical tool in clinical settings, which demands both pixel-level accuracy and high interpretability for diagnosis. The main challenges lie in that the construction of such AI models relies on substantial training data with fine-grained labeling that is impractical in real applications. To circumvent this barrier, we propose a prompt-driven constrained generative model to produce anatomically aligned healthy/diseased image pairs and then learn a superb pathology localization model in a supervised manner. The new paradigm effectively addresses the lack of high-fidelity chest X-ray images with pathology labeling at fine scales. Benefiting from the emerging text-driven generative foundation model and the newly incorporated constraint, our model presents promising localization accuracy of the subtle pathologies, high interpretability for clinical decisions, and good transfer ability to many unseen pathological categories (e.g., new prompts and mixed pathologies). These advantageous features establish our model as a promising solution to assist chest X-ray analysis. Besides, the proposed approach is also inspiring for other tasks lacking massive training data and time-consuming manual labeling.

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Section: Chest X-ray Image Generation With Ribs Controlmentioning

confidence: 99%

“…We build on the recent advances introduced in ControlNet 39 , endeavoring to integrate controllable functions--textual prompt of different diseases and rib skeleton-into the generation process 48,50 . Given a healthy image, the refined model can use its rib contours as a constraint and generate 51 the counterparts with specified diseases.…”

mentioning

confidence: 99%

Prompt-driven Healthy/Diseased Image Pairs Enabling Pixel-level Chest X-ray Pathology Localization

Suo,

Dong,

Cheng

et al. 2024

Preprint

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“…To this end, they borrow ideas from vertebrae segmentation [24] for sequential processing of ribs. A new rib benchmark data set with SOTA baseline scores was published in [33].…”

Section: Rib Segmentationmentioning

confidence: 99%

Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods

Berg

Vandersmissen

Wimmer

et al. 2023

Computers in Biology and Medicine

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“…The dataset was pre-split into training and validation sets, with 196 scans in the training set and 49 in the validation set. We refer the readers to Nguyen et al [19] for more details.…”

Section: Data Cohort 221 Vindr-ribcxr Datasetmentioning

confidence: 99%

An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis

Xü

Kevin

et al. 2023

Diagnostics

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Background: Suppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological lung details but are impractical due to the extremely long processing time. Machine learning (ML) methods are computationally efficient but are limited by the available ground truth (GT) for effective and robust training, resulting in suboptimal results. Purpose: To improve bone shadow suppression, we propose a generalizable yet efficient workflow for CXR rib suppression by combining physical and ML methods. Materials and Method: Our pipeline consists of two stages: (1) pair generation with GT bone shadows eliminated by a physical model in spatially transformed gradient fields; and (2) a fully supervised image denoising network trained on stage-one datasets for fast rib removal from incoming CXRs. For stage two, we designed a densely connected network called SADXNet, combined with a peak signal-to-noise ratio and a multi-scale structure similarity index measure as the loss function to suppress the bony structures. SADXNet organizes the spatial filters in a U shape and preserves the feature map dimension throughout the network flow. Results: Visually, SADXNet can suppress the rib edges near the lung wall/vertebra without compromising the vessel/abnormality conspicuity. Quantitively, it achieves an RMSE of ~0 compared with the physical model generated GTs, during testing with one prediction in <1 s. Downstream tasks, including lung nodule detection as well as common lung disease classification and localization, are used to provide task-specific evaluations of our rib suppression mechanism. We observed a 3.23% and 6.62% AUC increase, as well as 203 (1273 to 1070) and 385 (3029 to 2644) absolute false positive decreases for lung nodule detection and common lung disease localization, respectively. Conclusion: Through learning from image pairs generated from the physical model, the proposed SADXNet can make a robust sub-second prediction without losing fidelity. Quantitative outcomes from downstream validation further underpin the superiority of SADXNet and the training ML-based rib suppression approaches from the physical model yielded dataset. The training images and SADXNet are provided in the manuscript.

show abstract

VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

Cited by 5 publications

References 4 publications

Prompt-driven Healthy/Diseased Image Pairs Enabling Pixel-level Chest X-ray Pathology Localization

Prompt-driven Healthy/Diseased Image Pairs Enabling Pixel-level Chest X-ray Pathology Localization

Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods

An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis

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