# An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images Source: [An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:56:48.736Z Authors: Shuai Chen, Marleen de Bruijne Published: 2018-11-08T17:00:05.000Z ## Abstract Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence. In this paper, we propose a new end-to-end training method called Posterior-CRF. In contrast with previous approaches which use the original image intensity in the CRF, our approach applies 3D, fully connected CRF to the posterior probabilities from a CNN and optimizes both CNN and CRF together. The experiments on white matter hyperintensities segmentation demonstrate that our method outperforms CNN, post-processing CRF and different end-to-end training CRF approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()