File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Breast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis

TitleBreast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis
Authors
KeywordsBreast cancer
Breast tumor
Breast tumors
dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)
Feature extraction
fully convolutional network (FCN)
Image segmentation
image segmentation
image synthesis.
Learning systems
Task analysis
Tumors
Issue Date2021
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2021 How to Cite?
AbstractSegmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of breast cancer. However, variable shapes and sizes of breast tumors, as well as inhomogeneous background, make it challenging to accurately segment tumors in DCE-MR images. Therefore, in this article, we propose a novel tumor-sensitive synthesis module and demonstrate its usage after being integrated with tumor segmentation. To suppress false-positive segmentation with similar contrast enhancement characteristics to true breast tumors, our tumor-sensitive synthesis module can feedback differential loss of the true and false breast tumors. Thus, by following the tumor-sensitive synthesis module after the segmentation predictions, the false breast tumors with similar contrast enhancement characteristics to the true ones will be effectively reduced in the learned segmentation model. Moreover, the synthesis module also helps improve the boundary accuracy while inaccurate predictions near the boundary will lead to higher loss. For the evaluation, we build a very large-scale breast DCE-MR image dataset with 422 subjects from different patients, and conduct comprehensive experiments and comparisons with other algorithms to justify the effectiveness, adaptability, and robustness of our proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/325548
ISSN
2023 Impact Factor: 10.2
2023 SCImago Journal Rankings: 4.170
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Shuai-
dc.contributor.authorSun, Kun-
dc.contributor.authorWang, Li-
dc.contributor.authorQu, Liangqiong-
dc.contributor.authorYan, Fuhua-
dc.contributor.authorWang, Qian-
dc.contributor.authorShen, Dinggang-
dc.date.accessioned2023-02-27T07:34:12Z-
dc.date.available2023-02-27T07:34:12Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2021-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/325548-
dc.description.abstractSegmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of breast cancer. However, variable shapes and sizes of breast tumors, as well as inhomogeneous background, make it challenging to accurately segment tumors in DCE-MR images. Therefore, in this article, we propose a novel tumor-sensitive synthesis module and demonstrate its usage after being integrated with tumor segmentation. To suppress false-positive segmentation with similar contrast enhancement characteristics to true breast tumors, our tumor-sensitive synthesis module can feedback differential loss of the true and false breast tumors. Thus, by following the tumor-sensitive synthesis module after the segmentation predictions, the false breast tumors with similar contrast enhancement characteristics to the true ones will be effectively reduced in the learned segmentation model. Moreover, the synthesis module also helps improve the boundary accuracy while inaccurate predictions near the boundary will lead to higher loss. For the evaluation, we build a very large-scale breast DCE-MR image dataset with 422 subjects from different patients, and conduct comprehensive experiments and comparisons with other algorithms to justify the effectiveness, adaptability, and robustness of our proposed method.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.subjectBreast cancer-
dc.subjectBreast tumor-
dc.subjectBreast tumors-
dc.subjectdynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-
dc.subjectFeature extraction-
dc.subjectfully convolutional network (FCN)-
dc.subjectImage segmentation-
dc.subjectimage segmentation-
dc.subjectimage synthesis.-
dc.subjectLearning systems-
dc.subjectTask analysis-
dc.subjectTumors-
dc.titleBreast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2021.3129781-
dc.identifier.scopuseid_2-s2.0-85121376811-
dc.identifier.eissn2162-2388-
dc.identifier.isiWOS:000732239600001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats