کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6920182 | 1447877 | 2018 | 28 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fast anatomy segmentation by combining coarse scale multi-atlas label fusion with fine scale corrective learning
ترجمه فارسی عنوان
تقسیم بندی آناتومی سریع با ترکیب مقیاس درشت چندتایی با هماهنگی با آموزش مقدماتی خوب در مقیاس
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کلمات کلیدی
تقسیم چندتایی، یادگیری اصلاح شده، ثبت نام تصویر، فیوژن برچسب،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Deformable registration based multi-atlas segmentation has been successfully applied in a broad range of anatomy segmentation applications. However, the excellent performance comes with a high computational burden due to the requirement for deformable image registration and voxel-wise label fusion. To address this problem, we investigate the role of corrective learning (Wang et al., 2011) in speeding up multi-atlas segmentation. We propose to combine multi-atlas segmentation with corrective learning in a multi-scale analysis fashion for faster speeds. First, multi-atlas segmentation is applied in a low spatial resolution. After resampling the segmentation result back to the native image space, learning-based error correction is applied to correct systematic errors due to performing multi-atlas segmentation in a low spatial resolution. In cardiac CT and brain MR segmentation experiments, we show that applying multi-atlas segmentation in a coarse scale followed by learning-based error correction in the native space can substantially reduce the overall computational cost, with only modest or no sacrificing segmentation accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computerized Medical Imaging and Graphics - Volume 68, September 2018, Pages 16-24
Journal: Computerized Medical Imaging and Graphics - Volume 68, September 2018, Pages 16-24
نویسندگان
Hongzhi Wang, Deepika Kakrania, Hui Tang, Prasanth Prasanna, Tanveer Syeda-Mahmood,