Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model

Fooladivanda, Aida; Shokouhi, Shahriar B.; Mosavi, Mohammad Reza; Ahmadinejad, Nasrin

doi:10.1109/icbme.2014.7043932

Cited by 10 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, to improve the final mask, some more advanced approaches refine the results by using morphological operations [75–77]. Atlas‐based , that performs a direct comparison between the volumes to be segmented and entries of an anatomical atlas [25, 55, 78–80]. Although approaches falling in this group are usually very efficient when applied to a heterogeneous set of volumes, their effectiveness strongly relies on the population representativeness of the anatomical atlas and on the used acquisition protocol, limiting their portability and applicability. Geometrical‐based , which refines a mask obtained from a pixel‐based segmentation by using geometrical approaches (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Atlas‐based , that performs a direct comparison between the volumes to be segmented and entries of an anatomical atlas [25, 55, 78–80]. Although approaches falling in this group are usually very efficient when applied to a heterogeneous set of volumes, their effectiveness strongly relies on the population representativeness of the anatomical atlas and on the used acquisition protocol, limiting their portability and applicability.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

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%

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

View full text Add to dashboard Cite

show abstract

“…To counteract such limitations, the solutions are often restricted to a specific anatomical part. An example of such an application is provided by Fooladivanda et al [18], in which the authors use an atlas-based approach to segment the pectoral muscle, relying on a simpler pixel-based approach to segment the chest wall edge.…”

Section: Atlas-based Approachesmentioning

confidence: 99%

Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks

Berchiolli,

Wolfram,

Balachandran

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is regarded as one of the main diagnostic tools for breast cancer. Several methodologies have been developed to automatically localize suspected malignant breast lesions. Changes in tissue appearance in response to the injection of the contrast agent (CA) are indicative of the presence of malignant breast lesions. However, these changes are extremely similar to the ones of internal organs, such as the heart. Thus, the task of chest cavity segmentation is necessary for the development of lesion detection. In this work, a data-efficient approach is proposed, to automatically segment breast MRI data. Specifically, a study on several UNet-like architectures (Dynamic UNet) based on ResNet is presented. Experiments quantify the impact of several additions to baseline models of varying depth, such as self-attention and the presence of a bottlenecked connection. The proposed methodology is demonstrated to outperform the current state of the art both in terms of data efficiency and in terms of similarity index when compared to manually segmented data.

show abstract

“…Khalvati et al [20] employed a multi-atlas segmentation algorithm that is robust to intensity variations using phase congruency maps. Fooladivanda et al [21] introduced an atlas segmentation algorithm that uses both the pectoral muscle and the chest region. Accurate image registration and computation complexity limit the efficiency of the atlas-based methods for segmentation of the pectoral muscle.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images

Zafari

Diab

Eerola

et al. 2019

Advances in Visual Computing

View full text Add to dashboard Cite

Pectoral muscle segmentation is a crucial step in various computeraided applications of breast Magnetic Resonance Imaging (MRI). Due to imaging artifact and homogeneity between the pectoral and breast regions, the pectoral muscle boundary estimation is not a trivial task. In this paper, a fully automatic segmentation method based on deep learning is proposed for accurate delineation of the pectoral muscle boundary in axial breast MR images. The proposed method involves two main steps: pectoral muscle segmentation and boundary estimation. For pectoral muscle segmentation, a model based on the U-Net architecture is used to segment the pectoral muscle from the input image. Next, the pectoral muscle boundary is estimated through candidate points detection and contour segmentation. The proposed method was evaluated quantitatively with two realworld datasets, our own private dataset, and a publicly available dataset. The first dataset includes 12 patients breast MR images and the second dataset consists of 80 patients breast MR images. The proposed method achieved a Dice score of 95% in the first dataset and 89% in the second dataset. The high segmentation performance of the proposed method when evaluated on large scale quantitative breast MR images confirms its potential applicability in future breast cancer clinical applications.

show abstract

Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model

Cited by 10 publications

References 16 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

Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks

Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images

Contact Info

Product

Resources

About