rmsandu/segmentation-eval
Extract and evaluate radiomics for liver cancer tumors from DICOM segmentation masks. Using SimpleITK, PyRadiomics and PyDicom.
PythonNOASSERTION
Issues
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Send Screenshots of Axial and Coronal views for the 13 and 40 ml PAV/EAV volumes (same PAV but different EAV)
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Statistical tests :
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Fig 4 Manuscript Histograms
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Inner and Outer Ellipsoids
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Fig 3 Manuscript Pie-Chart:
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Figure 5 Ellipsoid Approximations (MIV + MEV):
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Altenative subplots presentations of the graphs
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Figure 1 Manuscript PAV vs EAV
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Scatter Plot with all groups
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Vessel proximity
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Re-compute Radiomics for M24 and B06
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Statistical analysis
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STL volumes for tumor and ablation on subcapsular lesions for liver surface segmentation
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Energy (kJ) scatter plots
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Paired Boxplots
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Flowchart scheme for README
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Histogram Image Readme
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Quantitative ablation pipeline
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More simplistic publication ready plots
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Deep Lesions vs Subcapsular markers marking
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Connected bar plot for ellipsoid volumes
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Probabilistic Ablation Modelling
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Sphericity index
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Ellipsoid fitting
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Linear Regression Model in Python
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Compute Metric for 177 Lesions
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Proximity to Vessels Category
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Plot ratio EAV:PAV (y-axis) w.r.t to the euclidean distance between the tumor center and needle target tip/ diameter of a tumor
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Change the interpolation method for griddata
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Plot by hospital centre
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Add Linear Regression line for each subgroup
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Boxplots EAV vs PAV
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EAV vs s PAV graphs :
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Predict Axes with Random Forest Model:
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Pie chart marker for scatter plot
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Add Ranges for Tumor Volumes and Chemo Cycles
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Publication Ready Graphs for Ablation and Energy
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Extract centroids from the segmentations
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Ellipsoid creation
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Apply Random Forest
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Missing Labe: ValueError('Label (%g) not present in mask. Choose from %s', 255, array([], dtype=uint32))
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Compute the ablation and tumor volume using the formula for the ellipsoid and plug-in the 3 axes
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Extract sphericity index ablation and tumors
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