The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Atlas probabilistik perut yang baik boleh memberikan maklumat penting untuk membimbing aplikasi segmentasi dan pendaftaran dalam perut. Di sini kami membina dan menguji atlas kebarangkalian menggunakan 24 imbasan CT perut dengan segmentasi manual pakar yang tersedia. Atlas dibina dengan memilih sasaran dan memetakan imbasan latihan lain ke sasaran tersebut dan kemudian menjumlahkan hasilnya ke dalam satu atlas kemungkinan. Kami menambah baik atlas perut kami sebelum ini dengan 1) memilih sasaran yang paling tidak berat sebelah seperti yang ditentukan oleh alat statistik, iaitu penskalaan multidimensi yang beroperasi pada tenaga lentur, 2) menggunakan set titik kawalan yang lebih baik untuk memodelkan ubah bentuk, dan 3) menggunakan kandungan maklumat yang lebih tinggi Imbasan CT dengan struktur hati dalaman yang boleh dilihat. Satu atlas dibina dalam ruang sasaran paling berat sebelah dan dua atlas dibina dalam ruang sasaran lain untuk perbandingan prestasi. Nilai atlas dinilai berdasarkan pembahagian yang terhasil; mana-mana atlas yang menghasilkan prestasi segmentasi terbaik dianggap atlas yang lebih baik. Kami mempertimbangkan dua kaedah pembahagian isipadu perut selepas pendaftaran dengan atlas kebarangkalian: 1) pembahagian mudah mengikut ambang atlas dan 2) penggunaan kaedah maksimum Bayesian a posteriori. Dengan menggunakan jackknifing, kami mengukur prestasi segmentasi ditambah atlas berkenaan dengan segmentasi pakar manual dan menunjukkan bahawa atlas yang dibina dalam ruang sasaran yang paling kurang berat sebelah menghasilkan prestasi segmentasi yang lebih baik daripada atlas yang dibina dalam ruang sasaran lain.
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Salinan
Hyunjin PARK, Alfred HERO, Peyton BLAND, Marc KESSLER, Jongbum SEO, Charles MEYER, "Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 8, pp. 2291-2301, August 2010, doi: 10.1587/transinf.E93.D.2291.
Abstract: A good abdominal probabilistic atlas can provide important information to guide segmentation and registration applications in the abdomen. Here we build and test probabilistic atlases using 24 abdominal CT scans with available expert manual segmentations. Atlases are built by picking a target and mapping other training scans onto that target and then summing the results into one probabilistic atlas. We improve our previous abdominal atlas by 1) choosing a least biased target as determined by a statistical tool, i.e. multidimensional scaling operating on bending energy, 2) using a better set of control points to model the deformation, and 3) using higher information content CT scans with visible internal liver structures. One atlas is built in the least biased target space and two atlases are built in other target spaces for performance comparisons. The value of an atlas is assessed based on the resulting segmentations; whichever atlas yields the best segmentation performance is considered the better atlas. We consider two segmentation methods of abdominal volumes after registration with the probabilistic atlas: 1) simple segmentation by atlas thresholding and 2) application of a Bayesian maximum a posteriori method. Using jackknifing we measure the atlas-augmented segmentation performance with respect to manual expert segmentation and show that the atlas built in the least biased target space yields better segmentation performance than atlases built in other target spaces.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2291/_p
Salinan
@ARTICLE{e93-d_8_2291,
author={Hyunjin PARK, Alfred HERO, Peyton BLAND, Marc KESSLER, Jongbum SEO, Charles MEYER, },
journal={IEICE TRANSACTIONS on Information},
title={Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans},
year={2010},
volume={E93-D},
number={8},
pages={2291-2301},
abstract={A good abdominal probabilistic atlas can provide important information to guide segmentation and registration applications in the abdomen. Here we build and test probabilistic atlases using 24 abdominal CT scans with available expert manual segmentations. Atlases are built by picking a target and mapping other training scans onto that target and then summing the results into one probabilistic atlas. We improve our previous abdominal atlas by 1) choosing a least biased target as determined by a statistical tool, i.e. multidimensional scaling operating on bending energy, 2) using a better set of control points to model the deformation, and 3) using higher information content CT scans with visible internal liver structures. One atlas is built in the least biased target space and two atlases are built in other target spaces for performance comparisons. The value of an atlas is assessed based on the resulting segmentations; whichever atlas yields the best segmentation performance is considered the better atlas. We consider two segmentation methods of abdominal volumes after registration with the probabilistic atlas: 1) simple segmentation by atlas thresholding and 2) application of a Bayesian maximum a posteriori method. Using jackknifing we measure the atlas-augmented segmentation performance with respect to manual expert segmentation and show that the atlas built in the least biased target space yields better segmentation performance than atlases built in other target spaces.},
keywords={},
doi={10.1587/transinf.E93.D.2291},
ISSN={1745-1361},
month={August},}
Salinan
TY - JOUR
TI - Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans
T2 - IEICE TRANSACTIONS on Information
SP - 2291
EP - 2301
AU - Hyunjin PARK
AU - Alfred HERO
AU - Peyton BLAND
AU - Marc KESSLER
AU - Jongbum SEO
AU - Charles MEYER
PY - 2010
DO - 10.1587/transinf.E93.D.2291
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2010
AB - A good abdominal probabilistic atlas can provide important information to guide segmentation and registration applications in the abdomen. Here we build and test probabilistic atlases using 24 abdominal CT scans with available expert manual segmentations. Atlases are built by picking a target and mapping other training scans onto that target and then summing the results into one probabilistic atlas. We improve our previous abdominal atlas by 1) choosing a least biased target as determined by a statistical tool, i.e. multidimensional scaling operating on bending energy, 2) using a better set of control points to model the deformation, and 3) using higher information content CT scans with visible internal liver structures. One atlas is built in the least biased target space and two atlases are built in other target spaces for performance comparisons. The value of an atlas is assessed based on the resulting segmentations; whichever atlas yields the best segmentation performance is considered the better atlas. We consider two segmentation methods of abdominal volumes after registration with the probabilistic atlas: 1) simple segmentation by atlas thresholding and 2) application of a Bayesian maximum a posteriori method. Using jackknifing we measure the atlas-augmented segmentation performance with respect to manual expert segmentation and show that the atlas built in the least biased target space yields better segmentation performance than atlases built in other target spaces.
ER -