Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing

Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing

Brain atrophy assessment in MRI, particularly of the hippocampus, is commonly used to support diagnosis and monitoring of dementia. Consequently, there is a demand for accurate automated hippocampus quantification. Most existing segmentation methods have been developed and validated on research data...

Deskribapen osoa

Xehetasun bibliografikoak
Egile Nagusiak:	Kalabizadeh, H, Griffanti, L, Yeung, P-H, Voets, N, Gillis, G, Mackay, C, Namburete, AI, Dinsdale, NK, Kamnitsas, K
Formatua:	Internet publication
Hizkuntza:	English
Argitaratua:	medRxiv 2024

Antzeko izenburuak

Spatial warping network for 3D segmentation of the hippocampus in MR images
nork: Dinsdale, N, et al.
Argitaratua: (2019)

Unlearning scanner bias for MRI harmonisation in medical image segmentation
nork: Dinsdale, NK, et al.
Argitaratua: (2020)

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation
nork: Dinsdale, NK, et al.
Argitaratua: (2022)

Anatomically plausible segmentations: explicitly preserving topology through prior deformations
nork: Wyburd, MK, et al.
Argitaratua: (2024)

TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
nork: Wyburd, MK, et al.
Argitaratua: (2021)

Omni-supervised domain adversarial training for white matter hyperintensity segmentation in the UK Biobank
nork: Sundaresan, V, et al.
Argitaratua: (2022)

Efficient Segment Anything on the Edge
nork: Stiles, Nicole
Argitaratua: (2024)

Segment anything in medical images
nork: Jun Ma, et al.
Argitaratua: (2024-01-01)

SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)
nork: Konstantinos Psychogyios, et al.
Argitaratua: (2023-01-01)

SFHarmony: source free domain adaptation for distributed neuroimaging analysis
nork: Dinsdale, NK, et al.
Argitaratua: (2024)

Prototype learning for explainable brain age prediction
nork: Hesse, LS, et al.
Argitaratua: (2024)

Unlearning scanner bias for MRI harmonisation
nork: Dinsdale, NK, et al.
Argitaratua: (2020)

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.
nork: Dinsdale, NK, et al.
Argitaratua: (2020)

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping
nork: Wenwen Li, et al.
Argitaratua: (2024-02-01)

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images
nork: Sundaresan, V, et al.
Argitaratua: (2021)

An efficient segment anything model for the segmentation of medical images
nork: Guanliang Dong, et al.
Argitaratua: (2024-08-01)

LIMITLESS : UPGRADE YOUR BRAIN, LEARN ANYTHING FASTER, AND UNLOCK YOUR EXCEPTIONAL LIFE /
nork: Kwik, Jim, author.
Argitaratua: (2020)

Adversarial attacks and robustness for segment anything model
nork: Liu, Shifei
Argitaratua: (2024)

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects
nork: Balboni, E, et al.
Argitaratua: (2022)

Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter
nork: Yaqin Li, et al.
Argitaratua: (2023-09-01)

Segment anything model for few-shot medical image segmentation with domain tuning
nork: Weili Shi, et al.
Argitaratua: (2024-11-01)

Segment Shards: Cross-Prompt Adversarial Attacks against the Segment Anything Model
nork: Shize Huang, et al.
Argitaratua: (2024-04-01)

Evaluating the impact of image preprocessing on iris segmentation
nork: José F. Valencia-Murillo, et al.
Argitaratua: (2014-08-01)

Segment anything: real-time AI model interaction and visualization
nork: Wang, Jingwei
Argitaratua: (2024)

Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation
nork: Ziquan Wang, et al.
Argitaratua: (2024-02-01)

The Segment Anything Model (SAM) for accelerating the smart farming revolution
nork: Alberto Carraro, et al.
Argitaratua: (2023-12-01)

Dual-Branch Fully Convolutional Segment Anything Model for Lesion Segmentation in Endoscopic Images
nork: Dongzhi He, et al.
Argitaratua: (2024-01-01)

Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers
nork: Mikhail Kulyabin, et al.
Argitaratua: (2024-09-01)

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation
nork: Luoyi Kong, et al.
Argitaratua: (2024-03-01)

AGSAM: Agent-Guided Segment Anything Model for Automatic Segmentation in Few-Shot Scenarios
nork: Hao Zhou, et al.
Argitaratua: (2024-04-01)

Evaluating segment anything model (SAM) on MRI scans of brain tumors
nork: Luqman Ali, et al.
Argitaratua: (2024-09-01)

Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties
nork: Efrain Torres-Lomas, et al.
Argitaratua: (2024-01-01)

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation
nork: Seena Joseph, et al.
Argitaratua: (2022-01-01)

Anything considered /
nork: 243740 Mayle, Peter
Argitaratua: (1997)

FedHarmony: unlearning scanner bias with distributed data
nork: Dinsdale, N, et al.
Argitaratua: (2022)

YOLOSAMIC: A Hybrid Approach to Skin Cancer Segmentation with the Segment Anything Model and YOLOv8
nork: Sevda Gül, et al.
Argitaratua: (2025-02-01)

Leaf only SAM: A segment anything pipeline for zero-shot automated leaf segmentation
nork: Dominic Williams, et al.
Argitaratua: (2024-08-01)

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
nork: Jiahao Liao, et al.
Argitaratua: (2024-04-01)

QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY
nork: E. O. Yilmaz, et al.
Argitaratua: (2024-03-01)

Challenges for machine learning in clinical translation of big data imaging studies
nork: Dinsdale, NK, et al.
Argitaratua: (2022)