Evaluation of intensity-based deformable registration of multi-parametric MRI for radiomics analysis of the prostate

Alley, Stephanie; Fedorov, Andrey; Ménard, Cynthia; Kadoury, Samuel

doi:10.1117/12.2549679

Cited by 4 publications

(3 citation statements)

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“…To further demonstrate the effectiveness of the proposed method, we also performed the comparison experiment with the intensity-based method [28]. The comparison results are given in Table 6.…”

Section: Performance Analysismentioning

confidence: 99%

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Gong

Xie

et al. 2022

Electronics

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Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis.

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Section: Performance Analysismentioning

confidence: 99%

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Gong

Xie

et al. 2022

Electronics

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“…First, a database of a large number (e.g., preferred to be greater than 1000) of medical images (mpMRI) is prepared so that a set of standardized images can be subject to radiomic analysis with minimal bias [ 82 , 83 , 84 ]. The number of imaging features number is preferred to be equal or less than the number of samples.…”

Section: Radiomics Pipeline For Predicting Tumor Gradementioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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“…DCNNs are popular for pixel-wise analysis and have shown the potential for application to PCa problems, including classification [9,10,12,13], organ or lesion segmentation [13][14][15][16], and registration [17]. These studies were mainly based on weakly supervised learning [9,12,18], which refers to the establishment of relations between GTs of patients and each slice of MRI as a supervised label.…”

Section: Related Workmentioning

confidence: 99%

Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists

Shao

Liu

Yan

et al. 2021

IEEE Trans. Biomed. Eng.

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The grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis and treatment system of prostate cancer. End-to-end method for stratifying the patient-level pathological appearance of prostate cancer (PCa) in magnetic resonance (MRI) are of high demand for clinical decision. Existing methods typically employ a statistical method for integrating slice-level results to a patient-level result, which ignores the asymmetric use of ground truth (GT) and overall optimization. Therefore, more domain knowledge (e.g. diagnostic logic of radiologists) needs to be incorporated into the design of the framework. The patient-level GT is necessary to be logically assigned to each slice of a MRI to achieve joint optimization between slice-level analysis and patient-level decision-making. In this paper, we propose a framework (PCa-GGNet-v2) that learns from radiologists to capture signs in a separate two-dimensional (2-D) space of MRI and further associate them for the overall decision, where all steps are optimized jointly in an end-to-end trainable way. In the training phase, patient-level prediction is transferred from weak supervision to supervision with GT. An association route records the attentional slice for reweighting loss of MRI slices and interpretability. We evaluate our method in an in-house multi-center dataset (N=570) and PROSTATEx (N=204), which yields fiveclassification accuracy over 80% and AUC of 0.804 at patient-level respectively. Our method reveals the state-of-

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Evaluation of intensity-based deformable registration of multi-parametric MRI for radiomics analysis of the prostate

Cited by 4 publications

References 0 publications

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists

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