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
Dalam pembedahan laparoskopi, kekurangan sensasi sentuhan dan maklum balas visual 3D menyukarkan untuk mengenal pasti kedudukan saluran darah secara intraoperatif. Koyak sebahagian yang tidak disengajakan atau saluran darah pecah sepenuhnya boleh mengakibatkan komplikasi yang serius; lebih-lebih lagi, jika pakar bedah tidak dapat menguruskan keadaan ini, pembedahan terbuka akan diperlukan. Pembezaan arteri daripada urat dan struktur lain dan keupayaan untuk mengesannya secara bebas mempunyai pelbagai aplikasi dalam prosedur pembedahan yang melibatkan kepala, leher, paru-paru, jantung, perut, dan anggota badan. Kami telah menggunakan pergerakan denyutan arteri untuk mengesan dan membezakan arteri daripada vena. Algoritma untuk pengesanan perubahan dalam kajian ini menggunakan pengesanan tepi untuk pendaftaran imej tanpa pengawasan. Kawasan yang berubah dikenal pasti dengan menolak imej sistolik dan diastolik. Sebagai langkah pasca pemprosesan, sifat rantau, termasuk purata warna, luas, panjang paksi utama dan kecil, perimeter dan kepejalan, digunakan sebagai input rangkaian LVQ (Pembelajaran Vektor Pengkuantitian). Output menghasilkan dua kelas objek: kawasan arteri dan bukan arteri. Selepas pasca pemprosesan, arteri boleh dikesan dalam bidang laparoskopi. Kaedah pendaftaran yang digunakan di sini dinilai berbanding dengan kaedah anjal linear dan bukan linear yang lain. Prestasi kaedah ini dinilai untuk pengesanan arteri dalam beberapa pembedahan laparoskopi pada model haiwan dan sebelas pesakit manusia. Kriteria penilaian prestasi adalah berdasarkan kadar negatif palsu dan positif palsu. Algoritma ini dapat mengesan kawasan arteri, walaupun dalam kes di mana arteri dikaburkan oleh tisu lain.
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Salinan
Hamed AKBARI, Yukio KOSUGI, Kazuyuki KOJIMA, "Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 3, pp. 498-505, March 2009, doi: 10.1587/transinf.E92.D.498.
Abstract: In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.498/_p
Salinan
@ARTICLE{e92-d_3_498,
author={Hamed AKBARI, Yukio KOSUGI, Kazuyuki KOJIMA, },
journal={IEICE TRANSACTIONS on Information},
title={Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection},
year={2009},
volume={E92-D},
number={3},
pages={498-505},
abstract={In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.},
keywords={},
doi={10.1587/transinf.E92.D.498},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection
T2 - IEICE TRANSACTIONS on Information
SP - 498
EP - 505
AU - Hamed AKBARI
AU - Yukio KOSUGI
AU - Kazuyuki KOJIMA
PY - 2009
DO - 10.1587/transinf.E92.D.498
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2009
AB - In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.
ER -