update

.gitignore

@@ -0,0 +1,10 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+
+# testing
+/coverage
+
+# misc
+package-lock.json

README.md

@@ -20,3 +20,25 @@ If you make changes and tag this project make sure that the version of the ```pa
   1. Create migration script for the database and save it the SPINE repository in the folder ```src/backedn/migrations_scripts/```. ***The migration script changes at least the document having the version of the json schema used.***
   1. Create a release for SPINE project including migration instructions from previous releases. Please refer to the migration script in the commit messages.
 
+## Testing with Jest and Ajv 
+All schemas should be validated with tests, based on Ajv library. Since Jest can’t 
+specify the error message, in order to display a meaningful error message, 
+we will have to create a custom matcher to show our own message, returned by Ajv.
+It is shown in [Example of matcher extension](/tests/jest.ajv.schema.test.js).
+
+### Test cases
+Examples used for testing are defined in "examples" subfolders. To identify weak tests 
+[Mutation tests](https://en.wikipedia.org/wiki/Mutation_testing) are applied (json files with _mutation._ prefix).
+Manual tools definitions can be tested using  [automatic suite](/tests/tools2.schema.test.js) 
+
+
+### Links:
+
+[Test JSON schema with AJV and Jest](https://medium.com/@moshfeu/test-json-schema-with-ajv-and-jest-c1d2984234c9 )
+
+[Getting started with JEST](https://jestjs.io/docs/en/getting-started)
+
+[Getting started with Ajv](https://github.com/ajv-validator/ajv)
+
+TODO:
+Set up coverage as in [Ajv-Istanbul](https://github.com/ajv-validator/ajv-istanbul)

examples/annotationWorkflow/tool_annotationThreeViewers.json

@@ -132,15 +132,41 @@
       	    "possibleImagesToDisplay": ["imagesA"],
             "hasDefaultImageToDisplay": false
       	  },
-      	  "displayControls":{
-      	  	"orientations": ["axial", "sagittal", "coronal"],
-      	  	"defaultOrientation": "axial",
-      	  	"smoothing": {
-      	  	  "smoothingControl": true,
-      	  	  "smoothingDefault": false
-      	  	}
-      	  }
-      	}
+          "displayControls": {
+            "orientation": {
+              "controlVisible": false,
+              "controlEnabled": false,
+              "options": [
+                {
+                  "label": "Sagittal",
+                  "value": "SAGITTAL"
+                },
+                {
+                  "label": "Coronal",
+                  "value": "CORONAL"
+                },
+                {
+                  "label": "Axial",
+                  "value": "AXIAL"
+                }
+              ],
+              "defaultValue": "AXIAL"
+            },
+            "smoothing": {
+              "controlVisible": false,
+              "controlEnabled": false,
+              "defaultValue": false
+            },
+            "sequence": {
+              "controlVisible": true,
+              "controlEnabled": true
+            },
+            "linked": {
+              "controlVisible": false,
+              "controlEnabled": false
+            }
+          }
+        }
       }
     },
 

examples/cohort/epvs.json

@@ -0,0 +1,19 @@
+{
+  "uuid": "5ec47681-0872-4368-a53f-aaed3f8ac391",
+  "docType": "cohort",
+  "name": "EPVS MICCAI VALDO 2021",
+  "description": "EPVS dataset",
+  "creationDate": "2021-09-07T17:04:06.158040",
+  "privacy": "private",
+  "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+  "co-investigators": [
+    "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+  ],
+  "logo": {
+    "type": "url",
+    "value": "https://www.miccai2021.org/files/images/layout/en/miccai2021-logo.png",
+    "url": "https://valdo.grand-challenge.org/Task3"
+  },
+  "relativePath": "EPVS_MICCAI_VALDO_2021",
+  "sourceSystem": "SPINE Insertion script"
+}

examples/cohort/epvs2.json

@@ -0,0 +1,30 @@
+{
+  "uuid": "8631a9d7-6247-412c-b921-a73769e16647",
+  "docType": "cohort",
+  "name": "EPVS MICCAI VALDO 2021",
+  "description": "EPVS dataset",
+  "creationDate": "2021-09-01T15:38:09.421140",
+  "privacy": "private",
+  "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+  "co-investigators": [
+    "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+  ],
+  "diseases": [],
+  "keywords": [
+    {
+      "label": "Brain",
+      "iri": "http://purl.org/sig/ont/fma/fma50801"
+    },
+    {
+      "label": "MRI",
+      "iri": "http://purl.bioontology.org/ontology/SNOMEDCT/113091000"
+    }
+  ],
+  "relativePath": "EPVS_MICCAI_VALDO_2021",
+  "logo": {
+    "type": "url",
+    "value": "https://www.miccai2021.org/files/images/layout/en/miccai2021-logo.png",
+    "url": "https://valdo.grand-challenge.org/Task1"
+  },
+  "sourceSystem": "SPINE Insertion script"
+}

examples/cohort/fat.json

@@ -0,0 +1,25 @@
+{
+  "uuid": "35d4d7d1d0fb89504a8c0c74250008af",
+  "docType": "cohort",
+  "name": "Abdominal Fat Testing",
+  "description": "Testing database for Abdominal Faat",
+  "creationDate": "2020-03-10T23:54:14.063Z",
+  "privacy": "public",
+  "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+  "co-investigators": [
+    "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+  ],
+  "diseases": [],
+  "keywords": [
+    {
+      "label": "Brain",
+      "iri": "http://purl.org/sig/ont/fma/fma50801"
+    },
+    {
+      "label": "MRI",
+      "iri": "http://purl.bioontology.org/ontology/SNOMEDCT/113091000"
+    }
+  ],
+  "relativePath": "Cohort_AbdominalFat_Testing",
+  "sourceSystem": "manualInsertion"
+}

examples/cohort/lacunes.json

@@ -0,0 +1,30 @@
+{
+  "uuid": "2be44462-b0a5-4a32-b3a7-cd33bcdff3f1",
+  "docType": "cohort",
+  "name": "LACUNES MICCAI VALDO 2021",
+  "description": "LACUNES dataset",
+  "creationDate": "2021-09-21T14:37:57.266647",
+  "relativePath": "LACUNES_MICCAI_VALDO_2021",
+  "privacy": "private",
+  "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+  "co-investigators": [
+    "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+  ],
+  "diseases": [],
+  "keywords": [
+    {
+      "label": "Brain",
+      "iri": "http://purl.org/sig/ont/fma/fma50801"
+    },
+    {
+      "label": "MRI",
+      "iri": "http://purl.bioontology.org/ontology/SNOMEDCT/113091000"
+    }
+  ],
+  "logo": {
+    "type": "url",
+    "value": "https://www.miccai2021.org/files/images/layout/en/miccai2021-logo.png",
+    "url": "https://valdo.grand-challenge.org/Task3"
+  },
+  "sourceSystem": "SPINE Insertion script"
+}

examples/cohort/miccai.json

@@ -0,0 +1,46 @@
+    {
+      "uuid": "023e3ce2-d2e0-4b73-87c1-c0c166b8c313",
+      "docType": "cohort",
+      "name": "MICROBLEEDS MICCAI VALDO 2021",
+      "description": "MICROBLEEDS dataset. he goal of this task is to develop an automated method to segment cerebral microbleeds in MRI scans. The training set contains segmentations of microbleeds in the full brain for every case.  The submitted automated methods will be applied on the hidden test set and should output microbleed segmentations  for the full brain. The predicted segmentation mask should be continuous and will be thresholded at 0.5 during evaluation to obtain a binary segmentation mask (0: background, 1: microbleed). An interesting public dataset that can be used to have additional training cases, is the microbleeds dataset that was released with the following paper: Q. Dou*, H. Chen*, L. Yu, J. Qin, L. Shi, P. A. Heng, et al. 'Automatic Detection of Cerebral Microbleeds from MR Images via 3D Convolutional Neural Networks'. IEEE Transactions on Medical Imaging (TMI), 2016.This set contains 20 cases with dot annotations of microbleeds.",
+      "creationDate": "2021-09-20T16:51:33.887301",
+      "privacy": "private",
+      "relativePath": "EPVS_MICCAI_VALDO_2021",
+      "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+      "co-investigators": [
+        "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+      ],
+      "diseases": [
+        {
+          "label": "Multiple Sclerosis",
+          "icdCode": "G35",
+          "url": "https://www.icd10data.com/search?s=G35"
+        },
+        {
+          "label": "Disseminated demyelination",
+          "icdCode": "G36",
+          "url": "https://www.icd10data.com/search?s=G36"
+        }
+      ],
+      "keywords": [
+        {
+          "label": "Multiple Sclerosis",
+          "iri": "http://purl.bioontology.org/ontology/SNOMEDCT/24700007"
+        },
+        {
+          "label": "Freesurfer",
+          "iri": "http://purl.org/sig/ont/fma/fma271960"
+        },
+        {
+          "label": "White Matter",
+          "iri": "http://purl.org/sig/ont/fma/fma83929"
+        }
+      ],
+      "logo": {
+        "type": "url",
+        "value": "https://www.miccai2021.org/files/images/layout/en/miccai2021-logo.png",
+        "url": "https://valdo.grand-challenge.org/Task2"
+      },
+      "sourceSystem": "SPINE Insertion script"
+    }
+

examples/cohort/miccai2.json

@@ -0,0 +1,30 @@
+{
+  "uuid": "20c2d549136e928edffcdb64b901f990",
+  "docType": "cohort",
+  "name": "Testing Openneuro. Dataset ds000221.",
+  "description": "Testing database from Openneuro",
+  "creationDate": "2019-08-21T19:32:14.063Z",
+  "privacy": "public",
+  "pi": "a60017ce-6be9-4054-9f84-06722d5166be",
+  "co-investigators": [
+    "0f8fc6f7-1361-4f55-9bfa-b8e70c3a5718"
+  ],
+  "relativePath": "CohortOpenneuro_ds0002221",
+  "sourceSystem": "manualInsertion",
+  "diseases": [],
+  "keywords": [
+    {
+      "label": "Brain",
+      "iri": "http://purl.org/sig/ont/fma/fma50801"
+    },
+    {
+      "label": "MRI",
+      "iri": "http://purl.bioontology.org/ontology/SNOMEDCT/113091000"
+    }
+  ],
+  "logo": {
+    "type": "url",
+    "value": "https://openneuro.org/assets/on-light-horz.23ff22c3.svg",
+    "url": "https://openneuro.org/datasets/ds000221/versions/1.0.0"
+  }
+}