کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6940062 | 869737 | 2016 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hierarchical local region-based sparse shape composition for liver segmentation in CT scans
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A hierarchical local region-based sparse shape composition for liver segmentation in CT scans A hierarchical local region-based sparse shape composition for liver segmentation in CT scans](/preview/png/6940062.png)
چکیده انگلیسی
Motivated by the goals of improving segmentation of challenging liver cases containing low contrast with neighboring organs and presence of pathologies as well as highly varied shapes between subjects, a novel framework is presented for liver segmentation in portal phase of abdominal CT images. In a first training step, we describe a multilevel local region-based Sparse Shape Composition (SSC) model, called MLR-SSC, to increase the flexibility of shape prior models and capture the detailed local shape information more faithfully. Specifically, the liver shapes are decomposed into multiple regions in a multilevel fashion. Moreover, we build a local shape repository for each region and refine an input shape in a region-by-region manner. In a second testing step, it starts with a blood vessel-based liver shape initialization to derive a more patient-specific initial shape, followed by a hierarchical deformable shape optimization algorithm. It makes the segmentation framework more efficient and robust to local minima. Extensive experiments on 60 clinical CT scans demonstrate that our method achieves much better accuracy and efficiency than two closely related methods in the presence of small training sets. Moreover, our method shows slightly superior performance to three newly published methods. Also, we compare our method with the published semi-automatic methods from the “MICCAI 2007 Grand Challenge” workshop.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 50, February 2016, Pages 88-106
Journal: Pattern Recognition - Volume 50, February 2016, Pages 88-106
نویسندگان
Changfa Shi, Yuanzhi Cheng, Fei Liu, Yadong Wang, Jing Bai, Shinichi Tamura,