diff --git a/notebooks/DaskComputationCoiled.ipynb b/notebooks/DaskComputationCoiled.ipynb
new file mode 100644
index 0000000..7bd6c5d
--- /dev/null
+++ b/notebooks/DaskComputationCoiled.ipynb
@@ -0,0 +1,758 @@
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/uncbiag/OAI_analysis_2/blob/pranjal3/notebooks/DaskComputationCoiled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FFTe-kxYbxW6"
+      },
+      "outputs": [],
+      "source": [
+        "# Install packages\n",
+        "\n",
+        "# Remember to Restart runtime after installation\n",
+        "\n",
+        "\n",
+        "!pip install itk==5.3rc4\n",
+        "!pip install vtk\n",
+        "!pip install itkwidgets\n",
+        "!pip install icon-registration==0.3.4\n",
+        "!pip install tornado==6.1\n",
+        "!pip install coiled\n",
+        "!pip install torch\n",
+        "!pip install jupyter\n",
+        "!pip install git+https://github.com/uncbiag/mermaid.git\n",
+        "!pip install git+https://github.com/uncbiag/easyreg.git\n",
+        "!pip install git+https://github.com/PranjalSahu/OAI_analysis_2.git#egg=oai_package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# All Imports\n",
+        "\n",
+        "import coiled\n",
+        "import dask\n",
+        "from dask import compute, visualize, delayed\n",
+        "\n",
+        "import itk\n",
+        "import vtk\n",
+        "import itkwidgets\n",
+        "from oai_analysis_2 import dask_processing as dp\n",
+        "\n",
+        "import os\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\""
+      ],
+      "metadata": {
+        "id": "GWZItOLfcoA4"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To create a software environment using docker with open gl installed\n",
+        "import coiled\n",
+        "coiled.create_software_environment(\n",
+        "    name=\"oai13\",\n",
+        "    container=\"pranjalsahu/pranjal-sahu-oai7:latest\",\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "jrqYtBziroJ7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 200
+        },
+        "outputId": "a767050b-561c-497b-9c60-de721fcde481"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[31mInvalid Coiled token encountered. You can create new tokens and manage your existing ones at \u001b[0m\n",
+              "\u001b[4;31mhttps://cloud.coiled.io/profile.\u001b[0m\n",
+              "\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">Invalid Coiled token encountered. You can create new tokens and manage your existing ones at </span>\n",
+              "<span style=\"color: #800000; text-decoration-color: #800000; text-decoration: underline\">https://cloud.coiled.io/profile.</span>\n",
+              "\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Please login to \u001b[4;94mhttps://cloud.coiled.io/profile\u001b[0m to get your token\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Please login to <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://cloud.coiled.io/profile</span> to get your token\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Token: ··········\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[32mAuthentication successful\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">Authentication successful</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Save credentials for next time? [Y/n]: Y\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Credentials have been saved at \u001b[34m/root/.config/dask/\u001b[0m\u001b[34mcoiled.yaml\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Credentials have been saved at <span style=\"color: #000080; text-decoration-color: #000080\">/root/.config/dask/coiled.yaml</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Creating new software environment\n",
+            "Creating container-only software environment\n",
+            "Successfully saved software environment build\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create Dask Client. This will also spawn dask worker and scheduler\n",
+        "\n",
+        "name = 'oai-processing11'\n",
+        "cluster = coiled.Cluster(n_workers=2,\n",
+        "                        name=name,\n",
+        "                        worker_vm_types=['c5a.4xlarge'],\n",
+        "                        scheduler_vm_types=['t3.medium'],\n",
+        "                        software='pranjal-sahu/oai13')\n",
+        "client = dask.distributed.Client(cluster, \n",
+        "                                  serializers=['pickle', 'dask'],\n",
+        "                                  deserializers=['pickle', 'dask'])\n",
+        "print(client)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 402,
+          "referenced_widgets": [
+            "a3a4c8e9ac194fdda8266fa1b8609667",
+            "777e25916eac4cac98f819685cb66c97"
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+        "outputId": "cc6172d3-bb16-48cd-d953-93cb310b816b"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
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+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a3a4c8e9ac194fdda8266fa1b8609667"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<Client: 'tls://172.18.0.2:8786' processes=0 threads=0, memory=0 B>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Main execution block\n",
+        "\n",
+        "phi_AB, image_A, image_B = dp.register_images_delayed()\n",
+        "deformed_fc = dp.deform_probmap_delayed(phi_AB, image_A, image_B, 'FC')\n",
+        "deformed_tc = dp.deform_probmap_delayed(phi_AB, image_A, image_B, 'TC')\n",
+        "\n",
+        "thickness_fc = dp.get_thickness(deformed_fc, 'FC')\n",
+        "thickness_tc = dp.get_thickness(deformed_tc, 'TC')\n",
+        "\n",
+        "result = [thickness_fc, thickness_tc]"
+      ],
+      "metadata": {
+        "id": "eucu3TABeG4r"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize Dask Computation Graph\n",
+        "\n",
+        "visualize(result)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "bdxMpRkNeJKE",
+        "outputId": "d5119b1f-d58a-497b-da3a-5c8dfd593a13"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Perform Computation\n",
+        "\n",
+        "output_result = compute(*result)"
+      ],
+      "metadata": {
+        "id": "u8-c4ggSeOmF"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#cluster.close()"
+      ],
+      "metadata": {
+        "id": "2a3YI46ArcRN"
+      },
+      "execution_count": 204,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Write the meshes as vtk files\n",
+        "\n",
+        "m1 = itk.mesh_from_dict(output_result[0])\n",
+        "m2 = itk.mesh_from_dict(output_result[1])\n",
+        "\n",
+        "itk.meshwrite(m1, 'm1.vtk')\n",
+        "itk.meshwrite(m2, 'm2.vtk')"
+      ],
+      "metadata": {
+        "id": "4ZdlKUlBfKy1"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Read the meshes for viewing\n",
+        "\n",
+        "v1 = vtk.vtkPolyDataReader()\n",
+        "v1.SetFileName('m1.vtk')\n",
+        "v1.Update()\n",
+        "m1 = v1.GetOutput()\n",
+        "\n",
+        "v2 = vtk.vtkPolyDataReader()\n",
+        "v2.SetFileName('m2.vtk')\n",
+        "v2.Update()\n",
+        "m2 = v2.GetOutput()"
+      ],
+      "metadata": {
+        "id": "XQu5lxJ5fS0-"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize the meshes\n",
+        "\n",
+        "from google.colab import output\n",
+        "output.enable_custom_widget_manager()\n",
+        "\n",
+        "itkwidgets.view(geometries=[m1, m2])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 717,
+          "referenced_widgets": [
+            "c7aa7935d75b439d838123e4587eafc4",
+            "db828dcbb20a483ca4564508b383d739"
+          ]
+        },
+        "id": "0Yueld1HfnY1",
+        "outputId": "b7385d7a-f766-460a-8b36-955e276b949a"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Viewer(geometries=[{'vtkClass': 'vtkPolyData', 'points': {'vtkClass': 'vtkPoints', 'name': '_points', 'numberO…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c7aa7935d75b439d838123e4587eafc4"
+            }
+          },
+          "metadata": {
+            "application/vnd.jupyter.widget-view+json": {
+              "colab": {
+                "custom_widget_manager": {
+                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
+                }
+              }
+            }
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/notebooks/FullDemo.ipynb b/notebooks/FullDemo.ipynb
index 19dd1c1..602824f 100644
--- a/notebooks/FullDemo.ipynb
+++ b/notebooks/FullDemo.ipynb
@@ -7,7 +7,7 @@
     "id": "view-in-github"
    },
    "source": [
-    "<a href=\"https://colab.research.google.com/github/PranjalSahu/OAI_analysis_2/blob/pranjal3/notebooks/FullDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/PranjalSahu/OAI_analysis_2/blob/master/notebooks/FullDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {
diff --git a/notebooks/coiled_requirements.txt b/notebooks/coiled_requirements.txt
new file mode 100644
index 0000000..7b49a3a
--- /dev/null
+++ b/notebooks/coiled_requirements.txt
@@ -0,0 +1,12 @@
+itk==5.3rc4
+vtk
+itkwidgets
+icon-registration==0.3.4
+tornado==6.1
+coiled
+torch
+jupyter
+girder_client
+git+https://github.com/uncbiag/mermaid.git
+git+https://github.com/uncbiag/easyreg.git
+git+https://github.com/PranjalSahu/OAI_analysis_2.git#egg=oai_package
diff --git a/oai_analysis_2/dask_processing.py b/oai_analysis_2/dask_processing.py
new file mode 100644
index 0000000..d628f5d
--- /dev/null
+++ b/oai_analysis_2/dask_processing.py
@@ -0,0 +1,166 @@
+# All Imports
+
+import numpy as np
+
+import os
+os.environ["CUDA_VISIBLE_DEVICES"]=""
+
+
+import coiled
+import dask
+from dask import delayed, compute, visualize
+from dask.distributed import Client, progress, LocalCluster
+
+
+
+@delayed(nout=3)
+def register_images_delayed():
+    import boto3
+    import itk
+    import icon_registration
+    import icon_registration.itk_wrapper as itk_wrapper
+    import icon_registration.pretrained_models as pretrained_models
+    from os.path import exists
+    import torch
+    torch.set_num_threads(8)
+    
+    image_A = 'image_preprocessed.nii.gz'
+    image_B = 'atlas_image.nii.gz'
+    if (exists(image_A) and exists(image_B)) == False:
+        s3          = boto3.resource("s3")
+        bucket_name = 'oaisample1'
+        bucket      = s3.Bucket(bucket_name)
+
+        s3.Bucket(bucket_name).download_file(image_A, image_A)
+        s3.Bucket(bucket_name).download_file(image_B, image_B)
+    
+    image_A = itk.imread(image_A, itk.D)
+    image_B = itk.imread(image_B, itk.D)
+
+    model = pretrained_models.OAI_knees_gradICON_model()
+    model.to('cpu')
+
+    # Register the images
+    phi_AB, phi_BA = itk_wrapper.register_pair(model, image_A, image_B)
+    return itk.dict_from_transform(phi_AB), itk.dict_from_image(image_A), itk.dict_from_image(image_B)
+
+
+@delayed
+def deform_probmap_delayed(phi_AB, image_A, image_B, image_type ='FC'):
+    import itk
+    import boto3
+
+    s3          = boto3.resource("s3")
+    bucket_name = 'oaisample1'
+    bucket      = s3.Bucket(bucket_name)
+    prob_file = str(image_type)+'_probmap.nii.gz'
+    s3.Bucket(bucket_name).download_file(prob_file, prob_file)
+    prob = itk.imread(prob_file, itk.D)
+
+    phi_AB1  = itk.transform_from_dict(phi_AB)
+    
+    def set_parameters(phi_AB, phi_AB1):
+        for i in range(len(phi_AB)-1):
+            transform1 = phi_AB1.GetNthTransform(i)
+
+            fp = phi_AB[i+1]['fixedParameters']
+            o1 = transform1.GetFixedParameters()
+            o1.SetSize(fp.shape[0])
+            for j, v in enumerate(fp):
+                o1.SetElement(j, v)
+            transform1.SetFixedParameters(o1)
+
+            p = phi_AB[i+1]['parameters']
+            o2 = transform1.GetParameters()
+            o2.SetSize(p.shape[0])
+            for j, v in enumerate(p):
+                o2.SetElement(j, v)
+            transform1.SetParameters(o2)
+
+    set_parameters(phi_AB, phi_AB1)
+    image_A = itk.image_from_dict(image_A)
+    image_B = itk.image_from_dict(image_B)
+
+    interpolator = itk.LinearInterpolateImageFunction.New(image_A)
+    
+    warped_image = itk.resample_image_filter(prob, 
+       transform=phi_AB1, 
+       interpolator=interpolator,
+       size=itk.size(image_B),
+       output_spacing=itk.spacing(image_B),
+       output_direction=image_B.GetDirection(),
+       output_origin=image_B.GetOrigin()
+    )
+
+    output_dict = itk.dict_from_image(warped_image)
+    return output_dict
+
+@delayed(nout=1)
+def get_thickness(warped_image, mesh_type):
+    import itk
+    import vtk
+    import numpy as np
+    from oai_analysis_2 import mesh_processing as mp
+
+    def get_itk_mesh(vtk_mesh):
+        Dimension = 3
+        PixelType = itk.D
+        
+        # Get points array from VTK mesh
+        points = vtk_mesh.GetPoints().GetData()
+        points_numpy = np.array(points).flatten()#.astype('float32')
+            
+        polys = vtk_mesh.GetPolys().GetData()
+        polys_numpy = np.array(polys).flatten()
+
+        # Triangle Mesh
+        vtk_cells_count = vtk_mesh.GetNumberOfPolys()
+        polys_numpy = np.reshape(polys_numpy, [vtk_cells_count, Dimension+1])
+
+        # Extracting only the points by removing first column that denotes the VTK cell type
+        polys_numpy = polys_numpy[:, 1:]
+        polys_numpy = polys_numpy.flatten().astype(np.uint64)
+
+        # Get point data from VTK mesh to insert in ITK Mesh
+        point_data_numpy = np.array(vtk_mesh.GetPointData().GetScalars())#.astype('float64')
+        
+        # Get cell data from VTK mesh to insert in ITK Mesh
+        cell_data_numpy = np.array(vtk_mesh.GetCellData().GetScalars())#.astype('float64')
+        
+        MeshType = itk.Mesh[PixelType, Dimension]
+        itk_mesh = MeshType.New()
+        
+        itk_mesh.SetPoints(itk.vector_container_from_array(points_numpy))
+        itk_mesh.SetCellsArray(itk.vector_container_from_array(polys_numpy), itk.CommonEnums.CellGeometry_TRIANGLE_CELL)
+        itk_mesh.SetPointData(itk.vector_container_from_array(point_data_numpy))
+        itk_mesh.SetCellData(itk.vector_container_from_array(cell_data_numpy))    
+        return itk_mesh
+
+    warped_image = itk.image_from_dict(warped_image)
+    distance_inner, distance_outer = mp.get_thickness_mesh(warped_image, mesh_type=mesh_type)
+    distance_inner_itk = get_itk_mesh(distance_inner)
+    distance_inner_itk_dict = itk.dict_from_mesh(distance_inner_itk)
+
+    return distance_inner_itk_dict
+
+@delayed(nout=2)
+def segment_image_delayed():
+    from os.path import exists
+    import boto3
+    import itk
+    from oai_analysis_2 import analysis_object as ao
+    import torch
+    torch.set_num_threads(8)
+
+    image_A = 'image_preprocessed.nii.gz'
+    if exists(image_A) ==  False:
+        s3          = boto3.resource("s3")
+        bucket_name = 'oaisample1'
+        bucket      = s3.Bucket(bucket_name)
+        s3.Bucket(bucket_name).download_file(image_A, image_A)
+
+    test_volume = itk.imread(image_A)
+    obj = ao.AnalysisObject()
+    FC_prob, TC_prob = obj.segment(test_volume)
+
+    return itk.dict_from_image(FC_prob), itk.dict_from_image(TC_prob)
\ No newline at end of file