B
Bishesh Khanal
Researcher at King's College London
Publications - 53
Citations - 528
Bishesh Khanal is an academic researcher from King's College London. The author has contributed to research in topics: Computer science & Rigid transformation. The author has an hindex of 11, co-authored 44 publications receiving 320 citations. Previous affiliations of Bishesh Khanal include French Institute for Research in Computer Science and Automation & University of Burgundy.
Papers
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Journal ArticleDOI
3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images
Benjamin Hou,Bishesh Khanal,Amir Alansary,Steven McDonagh,Alice Davidson,Mary A. Rutherford,Jo Hajnal,Daniel Rueckert,Ben Glocker,Bernhard Kainz +9 more
TL;DR: A learning-based image registration method capable of predicting 3-D rigid transformations of arbitrarily oriented 2-D image slices, with respect to a learned canonical atlas co-ordinate system is presented.
Book ChapterDOI
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.
Yuanwei Li,Amir Alansary,Juan J. Cerrolaza,Bishesh Khanal,Matthew Sinclair,Jacqueline Matthew,Chandni Gupta,Caroline L. Knight,Bernhard Kainz,Daniel Rueckert +9 more
TL;DR: This work proposes a new Patch-based Iterative Network (PIN), a multitask learning framework that combines regression and classification to improve localisation accuracy in 3D medical volumes and extends PIN to localise multiple landmarks by using principal component analysis, which models the global anatomical relationships between landmarks.
Book ChapterDOI
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li,Bishesh Khanal,Benjamin Hou,Amir Alansary,Juan J. Cerrolaza,Matthew Sinclair,Jacqueline Matthew,Chandni Gupta,Caroline L. Knight,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: This work proposes a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes and introduces additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy.
Book ChapterDOI
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li,Bishesh Khanal,Benjamin Hou,Amir Alansary,Juan J. Cerrolaza,Matthew Sinclair,Jacqueline Matthew,Chandni Gupta,Caroline L. Knight,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: In this article, an Iterative Transformation Network (ITN) was proposed to detect standard scan planes in 3D volumes of fetal brain ultrasound. But the standard plane detection in 3-D volume is a labour-intensive task and requires expert knowledge of fetal anatomy.
Journal ArticleDOI
A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease.
TL;DR: A detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model is focused on, which is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term.