2019 · 18 citations · 1 references
ComputingBiomedical EngineeringRadiologyComputer VisionDiagnostic ImagingDiagnostic Radiology3D ImagingKidney CancerMedical Image AnalysisMedical ImagingSemantic SegmentationVolume Segmentation3D Image Segmentation3D Medical Image Analysis3D Segmentation3D Semantic Segmentation3D Tumor DetectionOrgan SegmentationSegmentationTumor Segmentation
Automated detection and segmentation of kidney tumors from 3D CT images is very useful for doctors to make diagnosis and treatment plan. In this paper, we described a multi-stage semantic segmentation pipeline for kidney and tumor segmentation from 3D CT images based on 3D U-Net architecture. The current method can achieve 0.9674,0.8454 average dice for kidney and tumor and achieved the 2 nd place in the KiTS19 challenge.
1
nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.
Fabian Isensee, Jens Petersen, Simon Köhl et al. · arXiv (Cornell University) · 2019 · 153 citations · Full text