PERFORM Publications

Keyword search (4,163 papers available)

Title		Authors	PubMed ID
1	Towards user-centered interactive medical image segmentation in VR with an assistive AI agent	Spiegler P; Harirpoush A; Xiao Y;	41509996 ENCS
2	MedCLIP-SAMv2: Towards universal text-driven medical image segmentation	Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y;	40779830 ENCS
3	Exploring interaction paradigms for segmenting medical images in virtual reality	Jones Z; Drouin S; Kersten-Oertel M;	40402355 ENCS
4	MRI as a viable alternative to CT for 3D surgical planning of Cavitary bone tumors	Chae Y; Cheers GM; Kim M; Reidler P; Klein A; Fevens T; Holzapfel BM; Mayer-Wagner S;	40049253 ENCS
5	Open access segmentations of intraoperative brain tumor ultrasound images	Behboodi B; Carton FX; Chabanas M; de Ribaupierre S; Solheim O; Munkvold BKR; Rivaz H; Xiao Y; Reinertsen I;	39047165 SOH
6	FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images	Paul S; Patterson Z; Bouguila N;	38535151 ENCS
7	Impaired performance of rapid grip in people with Parkinson's disease and motor segmentation	Rebecca J Daniels	38507858 PSYCHOLOGY
8	PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging	Anstruther M; Rossini B; Zhang T; Liang T; Xiao Y; Fortin M;	37996857 SOH
9	Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data	Shamshiri MA; Krzyzak A; Kowal M; Korbicz J;	36758326 ENCS
10	Measures of motor segmentation from rapid isometric force pulses are reliable and differentiate Parkinson's disease from age-related slowing	Howard SL; Grenet D; Bellumori M; Knight CA;	35768733 PSYCHOLOGY
11	Spoken Word Segmentation in First and Second Language: When ERP and Behavioral Measures Diverge	Gilbert AC; Lee JG; Coulter K; Wolpert MA; Kousaie S; Gracco VL; Klein D; Titone D; Phillips NA; Baum SR;	34603133 PSYCHOLOGY
12	Sharp U-Net: Depthwise convolutional network for biomedical image segmentation	Zunair H; Ben Hamza A;	34348214 ENCS
13	LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images	Belasso CJ; Behboodi B; Benali H; Boily M; Rivaz H; Fortin M;	33097024 PERFORM
14	Two-stage ultrasound image segmentation using U-Net and test time augmentation.	Amiri M; Brooks R; Behboodi B; Rivaz H;	32350786 IMAGING
15	Statistical learning of multiple speech streams: A challenge for monolingual infants.	Benitez VL, Bulgarelli F, Byers-Heinlein K, Saffran JR, Weiss DJ	31444822 CONCORDIA
16	High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps.	Huck J, Wanner Y, Fan AP, Jäger AT, Grahl S, Schneider U, Villringer A, Steele CJ, Tardif CL, Bazin PL, Gauthier CJ	31278570 PSYCHOLOGY
17	The first MICCAI challenge on PET tumor segmentation.	Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P, Glatard T, Kain M, Yao Y, Barillot C, Kirov A, Visvikis D	29268169 IMAGING
18	A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort.	Xiao Y, Fonov V, Chakravarty MM, Beriault S, Al Subaie F, Sadikot A, Pike GB, Bertrand G, Collins DL	28491942 PERFORM

Title:	Open access segmentations of intraoperative brain tumor ultrasound images
Authors:	Behboodi B, Carton FX, Chabanas M, de Ribaupierre S, Solheim O, Munkvold BKR, Rivaz H, Xiao Y, Reinertsen I
Link:	https://pubmed.ncbi.nlm.nih.gov/39047165/
DOI:	10.1002/mp.17317
Publication:	Medical physics
Keywords:	brain tumor; intraoperative ultrasound; segmentations;
PMID:	39047165	Category:	Date Added:	2024-07-26
Dept Affiliation:	SOH 1 Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. 2 School of Health, Concordia University, Montreal, Canada. 3 Université Grenoble Alpes, CNRS, Grenoble INP, TIMC, Grenoble, France. 4 Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada. 5 Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway. 6 Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. 7 Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada. 8 Department of Health Research, SINTEF Digital, Trondheim, Norway. 9 Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Description:

Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.

Acquisition and validation methods: The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.

Data format and usage notes: Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.

Potential applications: The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.

BookR: School of Health Core Facilities Booking

"Segmentation" Keyword-tagged Publications:

Search Publications

No results