Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

  title={Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach},
  author={Xiangrong Zhou and Takuya Kano and Yunliang Cai and S. Li and Xinxin Zhou and Takeshi Hara and Ryujiro Yokoyama and Hiroshi Fujita},
  booktitle={SPIE Medical Imaging},
This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large… 

A hybrid approach for mammary gland segmentation on CT images by embedding visual explanations from a deep learning classifier into a Bayesian inference

The experimental results showed that the attention maps of the classifier successfully focused on the mammary gland regions on the CT images and could replace the atlas for supporting mammary glands segmentation, showing a higher computing efficiency, much better robustness, and easier implementation than the previous approach based on a probabilistic atlas.

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

The experimental results demonstrated the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images and the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples.

Fully automated breast density assessment from low-dose chest CT

A fully automated framework for breast density assessment from LDCT is presented and the continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman's rank correlation 0.91 (p-value < 0.001).

Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation

A novel method that combines automated deep learning based breast segmentation from CT thorax examinations with computation of breast glandularity based on radiodensity and volumetric breast density can offer reliable breast cancer risk measures with limited additional workload for the radiologist.

Fully automated gynecomastia quantification from low-dose chest CT

Encouraging results demonstrate the feasibility of fully automated gynecomastia quantification from LDCT, which may aid the early detection as well as the treatment of both gynehamastia and the underlying medical problems, if any, that cause gyne comastia.

A 023 Function Integrated Diagnostic Assistance Based on Multidisciplinary Computational Anatomy Models-Progress Overview FY 2015 -

The main purposes of the research in this project are to establish a scientific principle of multidisciplinary computational anatomy and to develop computer-aided diagnosis systems based on such anatomical models for organ and tissue functions.



Automated segmentation of mammary gland regions in non-contrast X-ray CT images

Segmentation of the whole breast from low-dose chest CT images

A fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test, achieves robust whole breast segmentation using an anatomy directed rule-based method.

Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique

The proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols, and showed no significant difference between the anatomy partitioning results from those two databases except regarding the spleen.

Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques

This chapter introduces an ensemble-learning-based approach that can be used to solve organ localization problems and has been used for localizing five different human organs in CT images, and the accuracy, robustness, and computational efficiency of the designed scheme were validated by experiments.

Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network

This paper proposes a general unsupervised groupwise segmentation: a direct simultaneous segmentation for a group of multi-modality, multi-chamber,multi-subject ( M3) cardiac images from a freely chosen imaging view.

Breast density: comparison of chest CT with mammography.

Preliminary results suggest that on further validation, breast density readings at CT may provide important additional risk information on CT of the chest and that computer-derived measurements may be helpful in such assessment.

Breast percent density: estimation on digital mammograms and central tomosynthesis projections.

High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced.

Rapid object detection using a boosted cascade of simple features

  • Paul A. ViolaMichael J. Jones
  • Computer Science
    Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
  • 2001
A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.

Breast imaging reporting and data system (BI-RADS).