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 ## Project Goals and Performance  
 The performance metrics requirements for this segmentation CNN are to achieve Dice Similarity Coefficient >0.90 and Jaccard Index >0.80 when comparing model predictions to ground truth segmentation masks.  
 
- ![report.dcm](/Section%203%20Simulate%20DIMSE/out/Study1_DCM%20Report%20Screenshot.jpg)  
- **Figure 1.** Example report output for Test Volumes Study 1, containing snapshots of identified hippocampus at different depths 
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 This project is broken into three sections and are located in separate folders:
 - Section 1 Curating a Dataset of Brain MRIs: Analyze Medical Segmentation Decathlon dataset metadata, analyze and visualize image volumes with corresponding labels, and identify and clean data that is not a brain MRI.  
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 In Section 3, the segmentation CNN from Section 2 will be integrated into a simulated clinical network.  This AI product will automatically compute hippocampus volume for brain MRI scans, and provide this information to clinicians in a DICOM report.  
 
-**Figure 2.** DIMSE Simulation Setup  
 
 List | Network Object	| Script to Simulate Network Object
 --- | --- | ---