Object Pose Estimation using Mid-level Visual Representations

Nejatishahidin, Negar; Pooya, Fayyazsanavi,; Košecká, Jana

doi:10.1109/iros47612.2022.9981452

Cited by 15 publications

(2 citation statements)

References 29 publications

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“…There exist differences in the regional patterns of progression in gliomas, as well as differences in 'overall survival' or 'progression-free-survival' (PFS) on a clinical level. Such a diverse landscape of these tumors suggests the need to develop imaging-based markers that are prognostic of patient outcome at the initial presentation of the disease to assist in building patient-centric treatment plans [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

Rathore

Khan

Ali³

et al. 2022

Applied Sciences

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Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

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Section: Introductionmentioning

confidence: 99%

“…They used the random forest regression model for prediction of OS in gliomas and reported a root mean square error of 311.5. Suter [2], used 4 MR images (T1, T2, T1c and FLAIR), extracted an initial set of 1353 features. They used deep learning CNN1 and CNN2 strategies.…”

Section: Introductionmentioning

confidence: 99%