Accurate and robust shape descriptors for the identification of RIB cage structures in CT-images with Random Forests

Gargouri, Mariem; Tierny, Julien; Jolivet, Erwan; Petit, Philippe; Angelini, Elsa D.

doi:10.1109/isbi.2013.6556413

Cited by 4 publications

(1 citation statement)

References 11 publications

(8 reference statements)

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“…In [33], pixels are classified using gray-scale and neighborhood structural information. In [34], rib-bones are extracted from CT images using Random Forest classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Atlas-based rib-bone detection in chest X-rays

Candemir

Jaeger

Bağcı

et al. 2016

Computerized Medical Imaging and Graphics

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This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91 ± 1.8 % for Montgomery and 85.48 ± 3.3 % for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.

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“…In [33], pixels are classified using gray-scale and neighborhood structural information. In [34], rib-bones are extracted from CT images using Random Forest classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Atlas-based rib-bone detection in chest X-rays

Candemir

Jaeger

Bağcı

et al. 2016

Computerized Medical Imaging and Graphics

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Rib segmentation in chest x-ray images based on unsupervised domain adaptation

Zhao,

Nie,

Shen

et al. 2023

Biomed. Phys. Eng. Express

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Rib segmentation in 2D chest x-ray images is a crucial and challenging task. On one hand, chest x-ray images serve as the most prevalent form of medical imaging due to their convenience, affordability, and minimal radiation exposure. However, on the other hand, these images present intricate challenges including overlapping anatomical structures, substantial noise and artifacts, inherent anatomical complexity. Currently, most methods employ deep convolutional networks for rib segmentation, necessitating an extensive quantity of accurately labeled data for effective training. Nonetheless, achieving precise pixel-level labeling in chest x-ray images presents a notable difficulty. Additionally, many methods neglect the challenge of predicting fractured results and subsequent post-processing difficulties. In contrast, CT images benefit from being able to directly label as the 3D structure and patterns of organs or tissues. In this paper, we redesign rib segmentation task for chest x-ray images and propose a concise and efficient cross-modal method based on unsupervised domain adaptation with centerline loss function to prevent result discontinuity and address rigorous post-processing. We utilize digital reconstruction radiography images and the labels generated from 3D CT images to guide rib segmentation on unlabeled 2D chest x-ray images. Remarkably, our model achieved a higher dice score on the test samples and the results are highly interpretable, without requiring any annotated rib markings on chest x-ray images. Our code and demo will be released in ‘https://github.com/jialin-zhao/RibsegBasedonUDA’.

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