Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object segmentation tasks. Most of them are a classification based approach, e.g. U-net, which predicts the probability of being target object or background for each voxel. One problem of those methods is lacking of topology guarantee for segmented objects, and usually post processing is needed to infer the boundary surface of the object. In this paper, a novel model based on convolutional neural network (CNN) followed by a learnable surface smoothing block is proposed to tackle the surface segmentation problem with end-to-end training. To the best of our knowledge, this is the first study to learn smoothness priors end-to-end with CNN for direct surface segmentation with global optimality. Experiments carried out on Spectral Domain Optical Coherence Tomography (SD-OCT) retinal layer segmentation and Intravascular Ultrasound (IVUS) vessel wall segmentation demonstrated very promising results.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical Image AnalysisObjectSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space
Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications.…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary and mutual influence between adjacent objects. The traditional graph-based optim…
Deep LearningImage SegmentationMedical Image SegmentationSegmentation+1A deep learning network with differentiable dynamic programming for retina OCT surface segmentation
Multiple-surface segmentation in Optical Coherence Tomography (OCT) images is a challenge problem, further complicated by the frequent presence of weak image boundaries. Recently, many deep learning (DL) based methods ha…
Model OptimizationSegmentationJust-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA). We present a novel interactive method calle…
MRI segmentationSegmentationAutomatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization
In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the proposed pipeline consisted of three steps. I…
HippocampusSegmentation