Medical Image Segmentation

Transcription

1 Medical Image Segmentation Xin Yang, HUST *Collaborated with UCLA Medical School and UCSB

2 Segmentation to Contouring ROI Aorta & Kidney 3D Brain MR Image 3D Abdominal CT Image Liver & Spleen Caudate Nucleus Abdominal Aorta & Aneurysm Bony Structure & Bone Marrow 2

3 Great advancements in general segmentation methods in computer vision domain However, gaps remain exist between methodological advances and clinical routine Various manual parameter settings difficult to find the right set of parameters even for algorithm developers A global parameter setting may hardly provide satisfactory result for every image location

4 Illustration of Active Contour s Limitations of for Vessel Segmentation Configuration A v=0.1, (x,y,z) = (94,85,217), r = 2.0,α=1.0,β=0.2, γ=5.0 Configuration B v=0.03, (x,y,z) = (94,85,217), r = 2.0,α=1.0,β=0.2, γ=5.0 Segmentation results obtained by two global configurations of Geodesic 4 Active Contours (GAC)

5 Segmentation Accuracy Segmentation Accuracy Optimized Parameter Setting is Location- Dependent optimal setting ACCA: Anterior Cerebral Circulation Arteries PCCA: Posterior Cerebral Circulation Arteries optimal setting Geodesic Active Contour 5

6 Segmentation Accuracy Segmentation Accuracy Optimized Parameter Setting is Location- Dependent, Database-dependent optimal setting optimal setting optimal setting optimal setting Geodesic Active Contour 6

7 Optimized Parameter Setting is Location- Dependent, Database-dependent Geodesic Active Contour Region Competition 7

8 Our Goal Bridge gap between methodological advances and clinical routine Segmentation methods easily accessible to clinicians with little mathematical expertise Design adaptive methods to automatically choose optimal parameter settings Reliable segmentation results for a wide range of data

9 Automatic Renal Compartment Segmentation in DCE-MRI Images Xin Yang, HUST *Collaborated with UCLA Medical School and UCSB 1. Yang et al, Automatic Renal Compartment Segmentation in DCE-MRI Images, In Proc. of MICCAI, Yang et al, Renal Compartment Segmentation and Functional Analysis in MR Urography, Medical Image Analysis (IF: 3.681), 2016.

10 Dynamic-Contrast Enhanced MRI Temporal Axis T T6... T4... T0... Spatial Axis Cortex Medulla Pelvis

11 Renal Functional Analysis Current diagnosis of renal dysfunction is based on measurements of creatinine, urea, electrolytes detectable after 60% function loss occurred DCE-MRI for accurate diagnosis of kidney disease at early stage Enhancement curve of a normal kidney Enhancement curve of a kidney with ureteropelvic junction obstruction ( 输尿管肾盂结合点阻塞 )

12 Methods which Rely on Learning Model Deform the model to fit current image Works fine for healthy kidneys But not for abnormal kidneys

13 Model Deform the model to fit current image

14 Methods which Rely on Intensities Difficult to select thresholds Optimal threshold is temporal point dependent, and data dependent for DCE-MRI images Our adaptive method Maximally Stable Temporal Volume (MSTV) Exploit both spatial and temporal correlation of a kidney region in DCE-MRI images

15 Maximally Stable Temporal Volume Segment image using a sequence of increasing thresholds Rather than selecting an optimal threshold which achieves the best segmentation

16 Maximally Stable Temporal Volume Segmentation when threshold is increasing Non-kidney segmentation is sensitive to threshold values Kidney segmentation remain stable across a wide range of consecutive threshold values Non-kidney segmentation is sensitive to threshold values Larger Threshold Kidney segmentation remain stable across a wide range of consecutive threshold values Larger Threshold

17 Maximally Stable Volume Detect Stable Structure Across a Wide Range of Consecutive Threshold Values via Connected Component Tree Tree Root Threshold 0 (all voxels) Children nodes Threshold 1 (voxels of several connected components) Children nodes Threshold i Leaf nodes Threshold k

18 Temporal Connectivity of Kidney Tissues Remain stable across T = i trees of the same level T = i+1 Remain stable across different levels of a tree Remain stable across different levels of a tree Maximally Stable Volume

19 DCE-MRI Data METV-based Kidney Segmentation K-mean clustering in Segmented Kidney t0, Right Kidney Left Kidney PCA Analysis on Entire Data tn, Principle Components 2-1

20 PCA-Kmeans Clustering for Renal Compartment Segmentation PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Fig. First 10 PCs of a dynamic DCE-MRI data Fig. Kmeans clustering and Recognition results

21 DCE-MRI Data METV-based Kidney Segmentation K-mean clustering in Segmented Kidney Results t0, Right Kidney Left Kidney PCA Analysis on Entire Data 1 Iterative Refinement 2-2 Segmented Cortex Segmented Medulla tn, Principle Components Segmented Pelvis

22 Iterative Refinement Key idea of our refinement method - Voxels of the same compartment are mostly highlighted at similar moments - Select a mostly enhanced temporal point as follows - Adaptive thresholding max 1 max max 1 MIE ( i) max S ( i) S ( i), S ( i) S ( i), S ( i) S ( i) i candidate cortex voxels cortex t b t b t b Before Refinement After Refinement

23 Evaluation Dataset with 16 kidney cases 7 normal cases, 7 disordered cases and 2 cases with operations where the medulla and pelvis were removed. Segmentation accuracy is evaluated using Dice Similarity Coefficient (DSC) We compare our method with three methods: Region Competition, our method without MSTV-based kidney segmentation (w/o MSTV), our method without PCA dimension reduction (w/o PCA) Table1. Comparison of Segmentation Methods Compartment Our method w/o MSTV w/o PCA Region Completion Disordered Kidneys Cortex Medulla Pelvis Kidneys with Op. Cortex Healthy Kidneys Cortex Medulla Pelvis

24 Ground truth Our Method w/o MSTV Visualization results of a health kidney w/o PCA Region competition

25 Ground truth Our method w/o MSTV Visualization results of a kidney with operation Where medulla and pelvis Were removed w/o PCA Region competition

26 Project website Code and demo video have been posted on the website