The automatic detection of lumber anatomy in epidural injections for ultrasound guidance
The purpose of this paper is to help the anesthesiologist to find the epidural depth automatically to make the first attempt to enter the path of the needle into the patient's body while it is clogged with bone and avoid causing a puncture in the surrounding areas of the patient`s back. In this regard, a morphology-based bone enhancement and detection followed by a Ramer-Douglas-Peucker algorithm and Hough transform is proposed. The proposed algorithm is tested on synthetic and real ultrasound images of laminar bone, and the results are compared with the template matching based Ligamentum Flavum (LF) detection method. Results indicate that the proposed method can faster detect the diagonal shape of the laminar bone and its corresponding epidural depth. Furthermore, the proposed method is reliable enough providing anesthesiologists with real-time information while an epidural needle insertion is performed. It has to be noted that using the ultrasound images is to help anesthesiologists to perform the blind injection, and due to quite a lot of errors occurred in ultrasound-imaging-based methods, these methods can not completely replace the tissue pressure-based method. And in the end, when the needle is injected into the area (dura space) measurements can only be trusted to the extent of tissue resistance. Despite the fairly limited amount of training data available in this study, a significant improvement of the segmentation speed of lumbar bones and epidural depth in ultrasound scans with a rational accuracy compared to the LF-based detection method was found.
Code (0)
등록된 구현이 없습니다.
Tasks
AnatomyTemplate MatchingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipelines, as it requires reasoning about pa…
BIG-bench Machine LearningEllipse Detection and Localization with Applications to Knots in Sawn Lumber Images
While general object detection has seen tremendous progress, localization of elliptical objects has received little attention in the literature. Our motivating application is the detection of knots in sawn timber images,…
object-detectionObject DetectionRegion ProposalLumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a da…
Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point of impracticality. In this paper, we in…
LumberChunker: Long-Form Narrative Document Segmentation
Modern NLP tasks increasingly rely on dense retrieval methods to access up-to-date and relevant contextual information. We are motivated by the premise that retrieval benefits from segments that can vary in size such tha…
ChunkingFormRAGRetrieval