S. Ghose, David M. Mills, J. Mitra, L. Smith, D. Yeo, A. Golby, Sarah F. Frisken, Thomas K. Foo
{"title":"神经外科术前和术中三维超声脑结构自动定位","authors":"S. Ghose, David M. Mills, J. Mitra, L. Smith, D. Yeo, A. Golby, Sarah F. Frisken, Thomas K. Foo","doi":"10.1117/12.2549630","DOIUrl":null,"url":null,"abstract":"Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes significant non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. Such mapping may aid in determining tumor resection margins during surgery. In this work, brain structures visible in pre- and intra-operative 3D ultrasound were used for automatic deformable registration. A Gaussian mixture model was used to automatically segment structures of interest in pre- and intra-operative ultrasound and patch-based normalized cross-correlation was used to establish correspondences between segmented structures. An affine registration based on correspondences was followed by B-spline based deformable registration to register pre- and intra-operative ultrasound. Manually labelled landmarks in pre- and intra-operative ultrasound were used to quantify the mean target registration error. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 pre- and intra-operative ultrasound image volumes of a public dataset.","PeriodicalId":302939,"journal":{"name":"Medical Imaging: Image-Guided Procedures","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automatic brain structure-guided registration of pre and intra-operative 3D ultrasound for neurosurgery\",\"authors\":\"S. Ghose, David M. Mills, J. Mitra, L. Smith, D. Yeo, A. Golby, Sarah F. Frisken, Thomas K. Foo\",\"doi\":\"10.1117/12.2549630\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes significant non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. Such mapping may aid in determining tumor resection margins during surgery. In this work, brain structures visible in pre- and intra-operative 3D ultrasound were used for automatic deformable registration. A Gaussian mixture model was used to automatically segment structures of interest in pre- and intra-operative ultrasound and patch-based normalized cross-correlation was used to establish correspondences between segmented structures. An affine registration based on correspondences was followed by B-spline based deformable registration to register pre- and intra-operative ultrasound. Manually labelled landmarks in pre- and intra-operative ultrasound were used to quantify the mean target registration error. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 pre- and intra-operative ultrasound image volumes of a public dataset.\",\"PeriodicalId\":302939,\"journal\":{\"name\":\"Medical Imaging: Image-Guided Procedures\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Imaging: Image-Guided Procedures\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2549630\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Imaging: Image-Guided Procedures","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2549630","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic brain structure-guided registration of pre and intra-operative 3D ultrasound for neurosurgery
Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes significant non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. Such mapping may aid in determining tumor resection margins during surgery. In this work, brain structures visible in pre- and intra-operative 3D ultrasound were used for automatic deformable registration. A Gaussian mixture model was used to automatically segment structures of interest in pre- and intra-operative ultrasound and patch-based normalized cross-correlation was used to establish correspondences between segmented structures. An affine registration based on correspondences was followed by B-spline based deformable registration to register pre- and intra-operative ultrasound. Manually labelled landmarks in pre- and intra-operative ultrasound were used to quantify the mean target registration error. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 pre- and intra-operative ultrasound image volumes of a public dataset.