Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conventionally via tedious and time-consuming manual radiographic procedures that are prone to subjectivity and observational variability. Reliability can be improved through the automatic detection and localization of spinal landmarks. To guide a CNN in the learning of spinal shape while detecting landmarks in X-ray images, we propose a novel loss based on a bipartite distance (BPD) measure, and show that it consistently improves landmark detection performance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to …
Facial Landmark DetectionregressionMulti-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident …
Medical Image AnalysisReinforcement Learning (RL)Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms. Motivated by established model-based fitting methods such…
Facial Landmark DetectionImage SegmentationMedical Image Segmentationregression+1Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces. Our DAC-CSR divides facial landmark detection…
Data AugmentationFace AlignmentFacial Landmark DetectionModel Selection+1Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network
Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccur…
Facial Landmark Detection