A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques in DCE-MRI

Marrone, Stefano; Piantadosi, Gabriele; Fusco, Roberta; Petrillo, Antonella; Sansone, Mario; Sansone, Carlo

doi:10.1109/cbms.2014.57

Cited by 14 publications

(4 citation statements)

References 25 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the aim of relieving the physician from this additional burden, the proposed CAD uses an automatic procedure, described in a previous work [90], to select the most suitable MC on a patient basis. The idea is to use a tracer‐kinetic based quality index (QI) [91] to rank all possible MCs, in order to take into account both spatial transformations and signal intensity variations (due to the contrast agent) in the selection of the best MC for a given study. Finally, as proposed in [92], we use a look‐up table and a sub‐sampling approach in order to speed‐up the QI evaluation without affecting the result reliability.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Piantadosi

Marrone

Fusco³

et al. 2018

IET Computer Vision

Self Cite

View full text Add to dashboard Cite

Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer-aided detection and diagnosis (CAD) system. Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma and to reduce motion artefacts. Among the major issues in developing a fully automated CAD, there are the accurate segmentation of lesions in regions of interest and their consequent staging (classification). This work introduces breast lesion automatic detection and diagnosis system (BLADeS), a comprehensive fully automated breast CAD aimed to support the radiologist during the patient diagnosis. The authors propose a hierarchical architecture that implements modules for breast segmentation, attenuation of motion artefacts, localisation of lesions and, finally, classification according to their malignancy. Performance was evaluated on 42 patients with histopathologically proven lesions, performing cross-validation to ensure a fair comparison. Results show that BLADeS can be successfully used to perform a fully automated breast lesion diagnosis starting from T1-weighted DCE-MRI, without requiring any operator interaction in any of the processing stages. 2 State-of-the-art In this section, a systematic literature review is performed, focusing on the analysis of breast cancer through dynamic contrastenhanced magnetic resonance imaging (DCE-MRI). The research has been performed by means of the following databases:

show abstract

Section: Methodsmentioning

confidence: 99%

Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Piantadosi

Marrone

Fusco³

et al. 2018

IET Computer Vision

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a consequence, motion-correction techniques (MCT) have attracted a great deal of attention in the breast, as well as in other organs, DCE-MRI [36,37]. The goal of an MCT is to re-align (register) each voxel in the post-contrast series to the corresponding one in the pre-contrast image.…”

Section: Motion Correctionmentioning

confidence: 99%

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

et al. 2021

Self Cite

View full text Add to dashboard Cite

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability.

show abstract

“…-Data sensitivity of medical records (privacy must be guaranteed) [1,2]; -Operational time comparable with clinical constraints [9,10]; -Massive data handling [11]; -Context scalability, since some sort of distributed elaboration and modularity should be applied [1,2].…”

Section: Introductionmentioning

confidence: 99%

Physicians’ Perceptions of the Use of a Chatbot for Information Seeking: Qualitative Study

Koman¹,

Fauvelle²,

Schück³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

The aim of this work is to investigate the effectiveness of novel human-machine interaction paradigms for eHealth applications. In particular, we propose to replace usual human-machine interaction mechanisms with an approach that leverages a chat-bot program, opportunely designed and trained in order to act and interact with patients as a human being. Moreover, we have validated the proposed interaction paradigm in a real clinical context, where the chat-bot has been employed within a medical decision support system having the goal of providing useful recommendations concerning several disease prevention pathways. More in details, the chat-bot has been realized to help patients in choosing the most proper disease prevention pathway by asking for different information (starting from a general level up to specific pathways questions) and to support the related prevention checkup and the final diagnosis. Preliminary experiments about the effectiveness of the proposed approach are reported.

show abstract

A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques in DCE-MRI

Cited by 14 publications

References 25 publications

Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

Physicians’ Perceptions of the Use of a Chatbot for Information Seeking: Qualitative Study

Contact Info

Product

Resources

About