diff --git "a/tutorials/UNet_with_SPIDER.ipynb" "b/tutorials/UNet_with_SPIDER.ipynb"
--- "a/tutorials/UNet_with_SPIDER.ipynb"
+++ "b/tutorials/UNet_with_SPIDER.ipynb"
@@ -1,20 +1,4768 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Fw5uqLfQvBB"
+      },
+      "source": [
+        "# Building a U-Net CNN Model for Magnetic Resonance Imaging (MRI) Segmentation"
+      ]
     },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4x3vZ46Quzq"
+      },
+      "source": [
+        "This tutorial will walk through building and training a U-Net convolutional neural network (CNN) model for 2D image segmentation of MRI data from scratch. We'll use the [SPIDER](https://huggingface.co/datasets/cdoswald/SPIDER) dataset containing 447 lumbar spine sagittal MRI scans for 218 unique patients.\n"
+      ]
     },
-    "language_info": {
-      "name": "python"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D5gcsQX9XgUZ"
+      },
+      "source": [
+        "## Why U-Net?\n",
+        "\n",
+        "U-Net was first introduced in a 2015 paper titled \"[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf)\" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox, at the University of Freiburg, Germany. Their paper develops both a model architecture (U-Net) and a training strategy (data augmentation and a weighted loss) that enables more precise image segmentation with relatively few training examples.\n"
+      ]
     },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "c9650600cd7343c292980b61d04eaf03": {
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kHeqYMxawGvX"
+      },
+      "source": [
+        "The model architecture looks like this:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMifvlBWuqiB"
+      },
+      "source": [
+        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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q4NOAjHvuz90"
+      },
+      "source": [
+        "The model has a \"contracting network\", which encodes higher-dimensional feature information as it reduces the spatial information, and an expanding network, which upsamples the spatial information to restore the original image size while also propagating the feature information to higher resolution layers. It also includes skip connections from the contracting to the expanding network. The model consists solely of 2D convolutional layers, ReLU activation functions, 2x2 max pooling layers, and 2x2 upsampling (tranposed convolutional) layers."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qi4EcJ7SW1wz"
+      },
+      "source": [
+        "## Downloading the SPIDER Dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lkJ8qkVhW96W"
+      },
+      "source": [
+        "First, we'll install the required dependencies and download the SPIDER dataset from HuggingFace:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FRSR1JBfIXwH",
+        "outputId": "e1ed5a74-35e9-4013-b894-b89f016c494e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create directories\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "import os\n",
+        "models_dir = '/content/drive/MyDrive/Colab Notebooks/Models'\n",
+        "os.makedirs(models_dir, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MqrP4hB2aMht",
+        "outputId": "05102928-fd6e-454e-bfd8-0e5a7dc55734"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install datasets -q\n",
+        "!pip install scikit-image -q\n",
+        "!pip install SimpleITK -q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "pxeqTBrWZ_XG",
+        "outputId": "a43d654e-fdf2-4fe6-a7a1-b473ee58d4fb"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f6a917c844104e719907c7a3b6d70221",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/22.5k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ffdc5ee8b1044fa9be361ca364837743",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/11.4k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "220753d48fca4af690da4f2f1dd617db",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/3.70G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3fac4fffe3b8455a9bc8bee2c21f4195",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/58.2M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "448fe058be884415bbcbdf113440c62a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data: 0.00B [00:00, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9ba23e240e544bf798ca84294c522651",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data: 0.00B [00:00, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7e5623ef40f84e718e41c18ced7b2c68",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1aae387154ba4403aeb0ad6ce09003d1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ae907ec7b8214ee59968398582245afb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8c773aa45974493b82523d5544403f54",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\n",
+        "    \"cdoswald/SPIDER\",\n",
+        "    name=\"default\", # Default configuration loads all 447 examples\n",
+        "    trust_remote_code=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll check the structure of the data to confirm that we have a train, validation, and test split:"
+      ],
+      "metadata": {
+        "id": "-LTs8QtqJ3MA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wnKvr-eYI-6N",
+        "outputId": "fe868b4c-e647-4a3a-d1ee-6cadc2a212b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['patient_id', 'scan_type', 'image', 'mask', 'image_path', 'mask_path', 'metadata', 'rad_gradings'],\n",
+              "        num_rows: 304\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['patient_id', 'scan_type', 'image', 'mask', 'image_path', 'mask_path', 'metadata', 'rad_gradings'],\n",
+              "        num_rows: 75\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['patient_id', 'scan_type', 'image', 'mask', 'image_path', 'mask_path', 'metadata', 'rad_gradings'],\n",
+              "        num_rows: 68\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BL6PRnDRQuQE"
+      },
+      "source": [
+        "## Defining the Model Architecture\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_L4WU2mBwfRO"
+      },
+      "source": [
+        "Next, let's specify the model architecture. This example uses [PyTorch](https://pytorch.org/tutorials/beginner/basics/intro.html), but you could build something similar with [Keras/TensorFlow](https://keras.io/getting_started/).\n",
+        "\n",
+        "We'll start by importing packages and checking if we have a GPU available for model training:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "AR1R7RpPv8Ao"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import numpy as np\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Aw1iuGLBHqr7",
+        "outputId": "e9066087-63a4-4b53-db57-16d2849f4cc0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cpu\n"
+          ]
+        }
+      ],
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_LhrratsxhLb"
+      },
+      "source": [
+        "We'll create an `EncodeBlock` class that will encode higher-dimensional feature information at each successive level of the model. The `EncodeBlock` uses two convolutional layers with a 3x3 kernel and padding to maintain the input image size after each convolution layer. The spatial information is downsampled using a 2x2 max pooling layer prior to the second, third, fourth, and fifth encoding blocks (see image of model architecture above)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Qq3Ytsenv6pt"
+      },
+      "outputs": [],
+      "source": [
+        "class EncodeBlock(nn.Module):\n",
+        "    \"\"\"Apply 2 successive layers of 3x3 convolutions and ReLU activation.\"\"\"\n",
+        "\n",
+        "    def __init__(self, in_channels, out_channels):\n",
+        "        super().__init__()\n",
+        "        self.encode = nn.Sequential(\n",
+        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x, maxpool_first=False):\n",
+        "        if maxpool_first:\n",
+        "            x = F.max_pool2d(x, kernel_size=2, stride=2)\n",
+        "        return self.encode(x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qd3-4dFzyrZV"
+      },
+      "source": [
+        "We'll also create a `DecodeBlock` class. This class will restore the original image dimensions (e.g., 512x512) through 2x2 [transpose convolution](https://www.cs.toronto.edu/~lczhang/321/lec/autoencoder_notes.html) layers, upsampling the spatial dimension. At each level, the upsampled higher-dimensional features will be concatenated with high resolution features from the corresponding encoding block, essentially allowing that information to skip subsequent encoding and decoding operations. Note that this concatenation operation requires that the shape of the outputs from the encoding block match the shape of the outputs from the upsampling operation in the decoding block (so we pad the upsampled outputs). Additionally, at each upsampling operation, we halve the number of feature dimensions for our decoded output prior to concatenating with the encoded output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "zjVzCH8MxVip"
+      },
+      "outputs": [],
+      "source": [
+        "class DecodeBlock(nn.Module):\n",
+        "    \"\"\"Upsample using 2x2 transposed convolution layers, concatenate with skip\n",
+        "    connections, and then apply 2 successive layers of 3x3 convolutions and ReLU.\"\"\"\n",
+        "\n",
+        "    def __init__(self, in_channels, out_channels):\n",
+        "        super().__init__()\n",
+        "        self.upsample = nn.ConvTranspose2d(\n",
+        "            in_channels,\n",
+        "            in_channels // 2,\n",
+        "            kernel_size=2,\n",
+        "            stride=2,\n",
+        "        )\n",
+        "        self.decode = nn.Sequential(\n",
+        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x, skip):\n",
+        "        x = self.upsample(x)\n",
+        "        # Crop skip tensor (based on\n",
+        "        # https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py)\n",
+        "        height_diff = skip.size()[2] - x.size()[2]\n",
+        "        width_diff = skip.size()[3] - x.size()[3]\n",
+        "        pad = [\n",
+        "            width_diff // 2, width_diff - width_diff // 2, # Pad last dimension\n",
+        "            height_diff // 2, height_diff - height_diff // 2, # Pad second-to-last dimension\n",
+        "        ]\n",
+        "        x = F.pad(x, pad)\n",
+        "        x = torch.cat([x, skip], dim=1)\n",
+        "        return self.decode(x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Finally, we'll put all of the encoder and decoder blocks together into the U-shaped architecture:"
+      ],
+      "metadata": {
+        "id": "iPhH9sZMQSny"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "9dZ7yrL615Zy"
+      },
+      "outputs": [],
+      "source": [
+        "class SimpleUNet(nn.Module):\n",
+        "    \"\"\"Basic U-Net model architecture.\"\"\"\n",
+        "\n",
+        "    def __init__(self, in_channels, n_classes):\n",
+        "        super().__init__()\n",
+        "\n",
+        "        # Create encoder (channel dimensions based on original U-Net paper)\n",
+        "        self.encode_1 = EncodeBlock(in_channels, 64)\n",
+        "        self.encode_2 = EncodeBlock(64, 128)\n",
+        "        self.encode_3 = EncodeBlock(128, 256)\n",
+        "        self.encode_4 = EncodeBlock(256, 512)\n",
+        "\n",
+        "        self.encode_last = nn.Sequential(\n",
+        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
+        "            nn.Conv2d(512, 1024, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Conv2d(1024, 1024, kernel_size=3, padding=1),\n",
+        "            nn.ReLU(),\n",
+        "        )\n",
+        "\n",
+        "        # Create decoder\n",
+        "        self.decode_1 = DecodeBlock(1024, 512)\n",
+        "        self.decode_2 = DecodeBlock(512, 256)\n",
+        "        self.decode_3 = DecodeBlock(256, 128)\n",
+        "        self.decode_4 = DecodeBlock(128, 64)\n",
+        "\n",
+        "        # Original paper used a 1x1 convolution to map features to n_classes\n",
+        "        self.decode_last = nn.Conv2d(64, n_classes, kernel_size=1) #1x1 Conv2d\n",
+        "\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Encoding\n",
+        "        x1 = self.encode_1(x)\n",
+        "        x2 = self.encode_2(x1, maxpool_first=True)\n",
+        "        x3 = self.encode_3(x2, maxpool_first=True)\n",
+        "        x4 = self.encode_4(x3, maxpool_first=True)\n",
+        "        x = self.encode_last(x4)\n",
+        "        # Decoding\n",
+        "        x = self.decode_1(x, skip=x4)\n",
+        "        x = self.decode_2(x, skip=x3)\n",
+        "        x = self.decode_3(x, skip=x2)\n",
+        "        x = self.decode_4(x, skip=x1)\n",
+        "        outputs = self.decode_last(x)\n",
+        "        return outputs\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ekqz1-J8Ar0"
+      },
+      "source": [
+        "## Training the Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rLsI2MZF-V6e"
+      },
+      "source": [
+        "To simplify the segmentation task for this tutorial, we'll use our model to segment only vertebrae vs. non-vertebrae pixels (as opposed to all of the types of anatomical structures that were segmented in the original SPIDER data). We'll use the momentum of 0.99 and batch size of 1 image used in the original paper. We'll also frame our problem as a multi-class classification problem and set our loss function as Cross Entropy (see this [helpful post](https://discuss.pytorch.org/t/understanding-channels-in-binary-segmentation/79966/2))."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "wjfOMdoL7ZVn"
+      },
+      "outputs": [],
+      "source": [
+        "# Set parameters from U-Net paper\n",
+        "momentum = 0.99\n",
+        "batch_size = 1\n",
+        "\n",
+        "# Set task-specific parameters\n",
+        "in_channels = 1     # grayscale images\n",
+        "n_classes = 2       # vertebrae/non-vertebrae\n",
+        "\n",
+        "# Create model\n",
+        "model = SimpleUNet(in_channels, n_classes)\n",
+        "model.to(device)\n",
+        "\n",
+        "# Create optimizer\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, betas=(momentum, 0.999))\n",
+        "\n",
+        "# Specify loss function\n",
+        "criterion = nn.CrossEntropyLoss()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Next we need to modify our segmentation masks so that they contain only 0's and 1's. We'll define a function that does just that:"
+      ],
+      "metadata": {
+        "id": "9z4UH_TpRt3B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "feelXkmLVHy7"
+      },
+      "outputs": [],
+      "source": [
+        "def convert_mask_to_binary(mask: torch.tensor, lower: int, upper: int) -> torch.tensor:\n",
+        "    \"\"\"Convert segmentation mask from [0, 225] to [0, 1].\n",
+        "    Lower and upper indicate the range of values that will be mapped\n",
+        "    to the value 1 (e.g., for vertebrae, lower=1 and upper=7). See\n",
+        "    SPIDER documentation for more details.\n",
+        "    \"\"\"\n",
+        "    return torch.where((mask < lower) | (mask > upper), 0, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we can start training. We'll loop over the `train` split of the SPIDER dataset loaded from HuggingFace and extract the image and mask 3-dimensional volumetric arrays. We'll take a single slice from each (depth=15/30) to make our job easier and reshape the image to (batch_size, channels, height, and width) and mask to (batch_size, height, width), since these are the order of the dimensions expected by our model. After iterating through all 304 training examples and recording the training loss, we'll calculate the validation loss for all 75 validation examples. If the loss decreases from our best loss to-date, then we'll save out the model parameters to use for inference later."
+      ],
+      "metadata": {
+        "id": "9SeqXKtGR2Z2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vPKVsiuh7ZQ9",
+        "outputId": "572cf9cc-29ee-448a-8391-395eefd29f99"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1 validation loss: 17.041\n",
+            "Model saved for epoch 1.\n",
+            "Epoch 2 validation loss: 15.912\n",
+            "Model saved for epoch 2.\n",
+            "Epoch 3 validation loss: 11.255\n",
+            "Model saved for epoch 3.\n",
+            "Epoch 4 validation loss: 8.397\n",
+            "Model saved for epoch 4.\n"
+          ]
+        }
+      ],
+      "source": [
+        "n_epochs = 40\n",
+        "lowest_loss = None\n",
+        "\n",
+        "for epoch in range(n_epochs):\n",
+        "    epoch_train_loss = 0\n",
+        "    epoch_val_loss = 0\n",
+        "\n",
+        "    # Training loop\n",
+        "    model.train()\n",
+        "    for batch_idx, train_data in enumerate(dataset['train']):\n",
+        "\n",
+        "        # Extract training image and mask (single slice from \"depth\" dim)\n",
+        "        train_image = torch.tensor(train_data['image'])[:,:,15].float()\n",
+        "        train_mask = torch.tensor(train_data['mask'])[:,:,15]\n",
+        "\n",
+        "        # Convert mask to binary (vertebrae/non-vertebrae)\n",
+        "        train_mask = convert_mask_to_binary(train_mask, 1, 7).reshape(512, 512)\n",
+        "\n",
+        "        # Convert input image to (batch_size, channels, height, width)\n",
+        "        train_image = train_image[None, None, :, :].to(device)\n",
+        "\n",
+        "        # Convert mask to (batch_size, height, width)\n",
+        "        train_mask = train_mask[None, :, :].to(device)\n",
+        "\n",
+        "        # Forward pass\n",
+        "        output = model(train_image)\n",
+        "\n",
+        "        # Backward pass\n",
+        "        optimizer.zero_grad()\n",
+        "        loss = criterion(output, train_mask)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # Record batch loss\n",
+        "        epoch_train_loss += loss.item()\n",
+        "\n",
+        "    # Validation loop\n",
+        "    model.eval()\n",
+        "    with torch.no_grad():\n",
+        "        for batch_idx, val_data in enumerate(dataset['validation']):\n",
+        "\n",
+        "            # Extract validation image and mask (single slice from \"depth\" dim)\n",
+        "            val_image = torch.tensor(val_data['image'])[:,:,15].float()\n",
+        "            val_mask = torch.tensor(val_data['mask'])[:,:,15]\n",
+        "\n",
+        "            # Convert mask to binary (vertebrae/non-vertebrae)\n",
+        "            val_mask = convert_mask_to_binary(val_mask, 1, 7)\n",
+        "\n",
+        "            # Convert input image to (batch_size, channels, height, width)\n",
+        "            val_image = val_image[None, None, :, :].to(device)\n",
+        "\n",
+        "            # Convert mask to (batch_size, height, width)\n",
+        "            val_mask = val_mask[None, :, :].to(device)\n",
+        "\n",
+        "            # Generate prediction\n",
+        "            output = model(val_image)\n",
+        "\n",
+        "            # Compute loss\n",
+        "            loss = criterion(output, val_mask)\n",
+        "\n",
+        "            # Record batch loss\n",
+        "            epoch_val_loss += loss.item()\n",
+        "\n",
+        "    # Print loss\n",
+        "    print(f'Epoch {epoch+1} validation loss: {round(epoch_val_loss, 3)}')\n",
+        "\n",
+        "    # Save model if improving\n",
+        "    if (lowest_loss is None) or (epoch_val_loss < lowest_loss):\n",
+        "        lowest_loss = epoch_val_loss\n",
+        "        save_model_path = os.path.join(models_dir, f'UNet_SPIDER_epoch{epoch+1}.pt')\n",
+        "        torch.save(model.state_dict(), save_model_path)\n",
+        "        print(f'Model saved for epoch {epoch+1}.')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Unfortunately, the Colab session timed out after only 4 epochs of training...but that's okay! Let's see how much our model learned in that time by predicting new masks for the test images."
+      ],
+      "metadata": {
+        "id": "p0I9pEIySjw0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Generating New Segmentation Masks for the Test Data"
+      ],
+      "metadata": {
+        "id": "Rk4WZpavCS-y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Rather than loading the full dataset again, we'll just load the first 10 images using the \"demo\" configuration:"
+      ],
+      "metadata": {
+        "id": "MpUl3REASuKx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\n",
+        "    \"cdoswald/SPIDER\",\n",
+        "    name=\"demo\", # Demo configuration loads 10 examples\n",
+        "    trust_remote_code=True,\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461,
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+        },
+        "id": "yOdFVP2hBtD-",
+        "outputId": "a71cabba-a876-44e4-d1fb-0ade5b4d219b"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/22.5k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b1abebb5f64d4445bfde112a031aeda1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/11.4k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7d7076366d264ac593aa02afe178b80d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/3.70G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f82daf0f6c344f55968126e6038b9d71"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/58.2M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fa8e3109629146528bcb16d5f099a3c9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2034b883e6c24edc8e17fc468df16e1a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bcae12f003884196ba2d334f104c445e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e4f17460a6ce49dab0c8a27e8e95a84a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ba27106f208e45e0a3cdc1523db20597"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cb11d600b0a24095b06a53780d9ef232"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "12c9378c7ad4450f97e4471d6932505a"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll also load our best model parameters from the model directory:"
+      ],
+      "metadata": {
+        "id": "corwckYjSzeb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "load_model_path = os.path.join(models_dir, 'UNet_SPIDER_epoch4.pt')\n",
+        "model.load_state_dict(torch.load(load_model_path, map_location=torch.device(device)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kg88_9BvCWuo",
+        "outputId": "69c22593-438c-49b5-e88c-d9ea3a7a1d4b"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<All keys matched successfully>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Similarly to how we looped over the training and validation splits of the SPIDER dataset, we can loop over images/masks from the test split, generate predictions, and calculate metrics like mean Intersection-over-Union (mIOU). Since we're only interested in a qualitative assessment of the model performance for this tutorial, we'll extract just the first image from the test set:"
+      ],
+      "metadata": {
+        "id": "Kjd7WpIoS5hQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "\n",
+        "        # Get first test image\n",
+        "        test_data = dataset['test'][0]\n",
+        "\n",
+        "        # Extract test image and mask (single slice from \"depth\" dim)\n",
+        "        test_image = torch.tensor(test_data['image'])[:,:,15].float()\n",
+        "        test_mask = torch.tensor(test_data['mask'])[:,:,15]\n",
+        "\n",
+        "        # Convert mask to binary (vertebrae/non-vertebrae)\n",
+        "        test_mask = convert_mask_to_binary(test_mask, 1, 7)\n",
+        "\n",
+        "        # Convert input image to (batch_size, channels, height, width)\n",
+        "        test_image = test_image[None, None, :, :].to(device)\n",
+        "\n",
+        "        # Convert mask to (batch_size, height, width)\n",
+        "        test_mask = test_mask[None, :, :].to(device)\n",
+        "\n",
+        "        # Generate prediction\n",
+        "        output = model(test_image)\n",
+        "        prediction = torch.argmax(output, 1)\n"
+      ],
+      "metadata": {
+        "id": "rqHSRENJDSdd"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Note that the output gives us probabilities of belonging to each class, so we took the argmax over the 1st dimension (the channels dimension) to create a binary {0, 1} prediction. Now we can compare our predicted mask against the actual mask for the test image:"
+      ],
+      "metadata": {
+        "id": "QrubfpdITV3s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "cd8oHF9-7ZHd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "d42fd7f1-d3ae-46ad-ce52-46f8da0a8b41"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig, ax = plt.subplots(1, 3, figsize=(12, 8))\n",
+        "ax[0].imshow(test_image.reshape(512, 512), cmap='gray')\n",
+        "ax[1].imshow(test_mask.reshape(512, 512), cmap='gray', vmin=0, vmax=1)\n",
+        "ax[2].imshow(prediction.reshape(512, 512), cmap='gray', vmin=0, vmax=1)\n",
+        "\n",
+        "ax[0].set_title(f'Test Image (Depth: 15/30)')\n",
+        "ax[1].set_title(f'Actual Mask (Depth: 15/30)')\n",
+        "ax[2].set_title(f'Predicted Mask (Depth: 15/30)')\n",
+        "\n",
+        "plt.show();"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Not bad for only 4 epochs! In future iterations of this tutorial, we'll experiment with various data augmentation techniques to improve model performance."
+      ],
+      "metadata": {
+        "id": "DuBIoPyRTkHc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CNZbAfXZm21Q"
+      },
+      "source": [
+        "### Sources\n",
+        "\n",
+        "\n",
+        "\n",
+        "- *van der Graaf, J.W., van Hooff, M.L., Buckens, C.F.M. et al. (2024) Lumbar spine segmentation in MR images: a dataset and a public benchmark. https://doi.org/10.1038/s41597-024-03090-w*\n",
+        "\n",
+        "- *Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. https://doi.org/10.1007/978-3-319-24574-4_28*\n",
+        "\n",
+        "- https://github.com/milesial/Pytorch-UNet/tree/master\n",
+        "\n",
+        "- https://discuss.pytorch.org/t/understanding-channels-in-binary-segmentation/79966/2"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
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-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Building a U-Net CNN Model for Spinal MRI Segmentation"
-      ],
-      "metadata": {
-        "id": "5Fw5uqLfQvBB"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "This tutorial will walk through building and training a simplified U-Net convolutional neural network (CNN)-type model for 3D image segmentation using the SPIDER dataset. We'll use PyTorch in this example."
-      ],
-      "metadata": {
-        "id": "b4x3vZ46Quzq"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Why a U-Net CNN Model?\n",
-        "\n",
-        "Lorum ipsum"
-      ],
-      "metadata": {
-        "id": "D5gcsQX9XgUZ"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [],
-      "metadata": {
-        "id": "50dpysUcX-96"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Downloading the SPIDER Dataset"
-      ],
-      "metadata": {
-        "id": "qi4EcJ7SW1wz"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "First, install the required dependencies and download the SPIDER dataset from HuggingFace."
-      ],
-      "metadata": {
-        "id": "lkJ8qkVhW96W"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install datasets -q\n",
-        "!pip install scikit-image -q\n",
-        "!pip install SimpleITK -q"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MqrP4hB2aMht",
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-      },
-      "execution_count": 2,
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
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-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
-            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-            "You will be able to reuse this secret in all of your notebooks.\n",
-            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
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-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "dfee95b4b6e64cc481cc76f8916a2b34"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating validation split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "1f061ec24adc4f6c84719cd282cd724c"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating test split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "47f4611f7d5446568004037a27220708"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "from datasets import load_dataset\n",
-        "\n",
-        "dataset = load_dataset(\n",
-        "    \"cdoswald/SPIDER\",\n",
-        "    name=\"default\", # Default configuration loads all 447 examples\n",
-        "    trust_remote_code=True,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [],
-      "metadata": {
-        "id": "BL6PRnDRQuQE"
-      }
-    }
-  ]
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file