SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans
원문 초록
This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes. SpineNetV2 improves over the original SpineNet software in two ways: (1) The vertebral body detection stage is significantly faster, more accurate and works across a range of fields-of-view (as opposed to just lumbar scans). (2) Radiological grading adopts a more powerful architecture, adding several new grading schemes without loss in performance. A demo of the software is available at the project website: http://zeus.robots.ox.ac.uk/spinenet2/.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Fast spline detection in high density microscopy data
DensePose From WiFi
Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding
Detector-in-Detector: Multi-Level Analysis for Human-Parts
토론
이 논문과 연결된 포럼 글이 아직 없습니다.