Nianhe Luo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nianhe Luo has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
Dariush Nikkhah
University College London, London, United Kingdom; Royal Free London NHS Foundation Trust, London, United Kingdom
2 shared publications- Petko Shtarbanov
University College London, London, United Kingdom
2 shared publications - Afshin Mosahebi
University College London, London, United Kingdom; Royal Free London NHS Foundation Trust, London, United Kingdom
1 shared publication Ahmed Yassin
Division of Surgery and Interventional Science, University College London, London, United Kingdom
1 shared publication- Anand S. Pandit
Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
1 shared publication - Faith Lee
UCL Medical School, University College London, London WC1E 6DE, UK
1 shared publication
Latest Publications
- Open Access
- 21 August 2025
- Published by Elsevier in Journal of Plastic, Reconstructive & Aesthetic Surgery
- Vol. 109, 181-186
Venous outflow augmentation protects against fat necrosis in the DIEP flap breast reconstruction: A retrospective analysis
- 21 August 2025
- Published by Elsevier in Journal of Plastic, Reconstructive & Aesthetic Surgery
- Vol. 109, 181-186
Open AccessVenous outflow augmentation protects against fat necrosis in the DIEP flap breast reconstruction: A retrospective analysis
- Open Access
- 16 October 2024
- Published by Elsevier in JPRAS Open
- Vol. 42, 329-333
Salvage of anterolateral thigh (ALT) flap with indocyanine green assessment of a flap pedicle and subsequent perforator-to-perforator anastomosis
- 16 October 2024
- Published by Elsevier in JPRAS Open
- Vol. 42, 329-333
Open AccessSalvage of anterolateral thigh (ALT) flap with indocyanine green assessment of a flap pedicle and subsequent perforator-to-perforator anastomosis
- Open Access
A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation
The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.
Open AccessA Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation
The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.