DeepCAC2 submission

models/deepcac2/config/default.yml

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+general:
+  data_base_dir: /app/data
+  version: 1.0
+  description: custom DeepCAC2 starting from dicom and exporting AGS and RISK only for non-mapped cac segmentations
+
+execute:
+- DicomImporter
+- NiftiConverter
+- NNUnetRunner
+- DeepCACRunner
+- AGSCalculator
+- ReportExporter
+- module: ReportExporter
+  globalreport: true
+  meta:
+    scope: global 
+- DataOrganizer
+
+modules:
+
+  DicomImporter:
+    meta:
+      mod: '%Modality'
+      pid: '%PatientID'
+
+  NNUnetRunner:
+    folds: all
+    nnunet_task: Task400_OPEN_HEART_1FOLD
+    nnunet_model: 3d_lowres
+    roi: HEART
+
+  ReportExporter:
+    meta:
+      mod: report
+      scope: instance
+    includes:
+    - attr: sid
+      label: SeriesInstanceUID
+    - attr: pid
+      label: PatientID
+    - data: ags
+      label: Predicted Agatston Score
+      value: value
+    - data: rc
+      label: Predicted Risk Group
+      value: value
+
+  DataOrganizer:
+    targets:
+    - nrrd:mod=seg:roi=CAC AND NOT any:variant=mapped-->[i:sid]/nrrd/cac.seg.nrrd
+    - nrrd:mod=seg:roi=CAC:variant=mapped-->[i:sid]/nrrd/mcac.seg.nrrd
+    - json:mod=report:scope=instance-->[i:sid]/DeepCAC.report.json
+    - json:mod=report:scope=global-->DeepCAC.report.json

models/deepcac2/config/pid_nifti.yml

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+
+general:
+  data_base_dir: /app/data
+  version: 1.0
+  description: DeepCAC2 starting from nifti file(s) named by PatientID and exporting AGS and RISK for mapped and non-mapped cac segmentations.
+
+execute:
+- FileStructureImporter
+- NNUnetRunner
+- DeepCACRunner
+#- CACMapping
+- AGSCalculator
+- ReportExporter
+- module: ReportExporter
+  globalreport: true
+  meta:
+    scope: global 
+- DataOrganizer
+
+modules:
+
+  FileStructureImporter:
+    input_dir: 'input_data'
+    import_id: _instance
+    structures: 
+    - re:(\d+).nii.gz::$pid@instance@nifti:mod=ct
+
+  NNUnetRunner:
+    folds: all
+    nnunet_task: Task400_OPEN_HEART_1FOLD
+    nnunet_model: 3d_lowres
+    roi: HEART
+
+  ReportExporter:
+    meta:
+      mod: report
+      scope: instance
+    includes:
+    - attr: pid
+      label: PatientID
+    - data: ags
+      label: Agatston Score Description
+      value: description
+    - data: ags
+      label: Predicted Agatston Score
+      value: value
+    - data: rc
+      label: Risk Group Description
+      value: description
+    - data: rc
+      label: Predicted Risk Group
+      value: value
+    # - data: mags
+    #   label: Mapped Predicted Agatston Score
+    #   value: value
+    # - data: mrc
+    #   label: Mapped Predicted Risk Group
+    #   value: value
+
+  DataOrganizer:
+    targets:
+    #- nrrd:mod=seg:roi=CAC AND NOT any:variant=mapped-->[i:pid]/cac.seg.nrrd
+    #- nrrd:mod=seg:roi=CAC:variant=mapped-->[i:pid]/mcac.seg.nrrd
+    #- json:mod=report:scope=instance-->[i:pid]/DeepCAC.report.json
+    - json:mod=report:scope=global-->DeepCAC.report.json

models/deepcac2/dockerfiles/Dockerfile

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+FROM mhubai/base:latest
+
+# FIXME: set this environment variable as a shortcut to avoid nnunet crashing the build
+# by pulling sklearn instead of scikit-learn
+# N.B. this is a known issue:
+# https://github.com/MIC-DKFZ/nnUNet/issues/1281 
+# https://github.com/MIC-DKFZ/nnUNet/pull/1209
+ENV SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True
+
+# Install dependencies
+RUN pip3 install --no-cache-dir \
+    nnunet==1.7.1 \
+    torch==2.0.1 \
+    torchvision==0.15.2 \
+    torchio==0.19.1 \
+ && pip3 install --no-cache-dir --force-reinstall \
+    "dicom2nifti<2.6" \
+    numpy==1.26.4 \
+    pydicom==2.4.4
+
+# pull weights for platipy's nnU-Net so that the user doesn't need to every time a container is run
+ENV WEIGHTS_DIR="/root/.platipy/nnUNet_models/nnUNet/"
+ENV WEIGHTS_URL="https://zenodo.org/record/6585664/files/Task400_OPEN_HEART_3d_lowres.zip"
+ENV WEIGHTS_FN="Task400_OPEN_HEART_3d_lowres.zip"
+
+RUN wget --directory-prefix ${WEIGHTS_DIR} ${WEIGHTS_URL}
+RUN unzip ${WEIGHTS_DIR}${WEIGHTS_FN} -d ${WEIGHTS_DIR}
+RUN rm ${WEIGHTS_DIR}${WEIGHTS_FN}
+
+# specify nnunet specific environment variables
+ENV WEIGHTS_FOLDER=$WEIGHTS_DIR
+
+# Import the MHub model definition
+ARG MHUB_MODELS_REPO
+RUN buildutils/import_mhub_model.sh deepcac2 ${MHUB_MODELS_REPO}
+
+# download the DeepCAC2 model weights
+ENV DEEPCAC2_WEIGHTS_DIR="/app/models/deepcac2/src/weights/"
+ENV DEEPCAC2_WEIGHTS_FN="vivid-haze-6_model.pt"
+ENV DEEPCAC2_WEIGHTS_URL="https://www.dropbox.com/scl/fi/4rodcp9y2vula8loh1v6r/vivid-haze-6_model.pt?rlkey=7j3cyaltvdpthukchn5xbh01d&dl=1"
+RUN mkdir -p ${DEEPCAC2_WEIGHTS_DIR} \
+ && wget -o /dev/stdout -O ${DEEPCAC2_WEIGHTS_DIR}${DEEPCAC2_WEIGHTS_FN} ${DEEPCAC2_WEIGHTS_URL}
+
+# Default run script
+ENTRYPOINT ["mhub.run"]
+CMD ["--workflow", "default"]

models/deepcac2/meta.json

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+{
+  "id": "7c7c7e7b-3c4d-4a84-9a45-5a6c2b46c1a1",
+  "name": "deepcac2",
+  "title": "DeepCAC2",
+  "summary": {
+    "description": "DeepCAC2 is a two-stage deep learning pipeline for coronary artery calcification assessment in chest CT. It first uses an nnU-Net-based cardiac segmentation model to localize the heart, then applies a patch-based 3D U-Net to segment coronary artery calcifications and derive Agatston calcium scores and risk categories.",
+    "inputs": [
+      {
+        "label": "Chest CT",
+        "description": "Non-contrast, low-dose chest CT scan in DICOM format.",
+        "format": "DICOM",
+        "modality": "CT",
+        "bodypartexamined": "CHEST",
+        "slicethickness": "1-5 mm",
+        "non-contrast": true,
+        "contrast": false
+      }
+    ],
+    "outputs": [
+      {
+        "type": "Segmentation",
+        "classes": [
+          "heart",
+          "coronary artery calcification"
+        ]
+      },
+      {
+        "type": "Prediction",
+        "valueType": "float",
+        "label": "Predicted Agatston Score",
+        "description": "Automated coronary artery calcium score derived from the predicted CAC segmentation."
+      },
+      {
+        "type": "Classification",
+        "classes": [
+          "very low",
+          "low",
+          "moderate",
+          "high"
+        ]
+      }
+    ],
+    "model": {
+      "architecture": "Two-stage pipeline with nnU-Net-based cardiac segmentation followed by patch-based 3D U-Net CAC segmentation.",
+      "training": "supervised",
+      "cmpapproach": "3D"
+    },
+    "data": {
+      "training": {
+        "vol_samples": 622
+      },
+      "evaluation": {
+        "vol_samples": 390
+      },
+      "public": true,
+      "external": true
+    }
+  },
+  "details": {
+    "name": "DeepCAC2",
+    "version": "1.0.0",
+    "devteam": "Artificial Intelligence in Medicine Program, Mass General Brigham / Harvard Medical School",
+    "type": "Segmentation and prediction",
+    "date": {
+      "weights": "2026-01-01",
+      "code": "2026-01-01",
+      "pub": "2026-03-25"
+    },
+    "cite": "Nürnberg L, Bernatz S, Foldyna B, Lu MT, Fedorov A, Aerts HJWL. Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2).",
+    "license": {
+      "code": "MIT",
+      "weights": "MIT"
+    },
+    "publications": [
+      {
+        "title": "Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)",
+        "uri": "https://arxiv.org/"
+      }
+    ],
+    "github": "https://github.com/MHubAI/models/tree/main/models/deepcac2",
+    "slicer": false
+  },
+  "info": {
+    "use": {
+      "title": "Intended use",
+      "text": "DeepCAC2 is designed for automated coronary artery calcification (CAC) segmentation and cardiovascular risk assessment from non-contrast chest CT scans, enabling opportunistic screening without dedicated cardiac CT protocols. The generated outputs and dataset can be explored via an interactive public dashboard [1] and are derived from the National Lung Screening Trial (NLST) cohort available through the Imaging Data Commons (IDC) [2].",
+      "references": [
+        {
+          "label": "DeepCAC2 interactive dashboard",
+          "uri": "https://lookerstudio.google.com/reporting/2c656fab-89ce-4ccd-a6a6-6c76ba7ecc51/page/P3qpF"
+        },
+        {
+          "label": "Imaging Data Commons (NLST collection)",
+          "uri": "https://imaging.datacommons.cancer.gov/"
+        }
+      ]
+    },
+    "analyses": {
+      "title": "Analyses",
+      "text": "The pipeline produces volumetric segmentations of the heart and coronary artery calcifications, from which Agatston calcium scores (CACS) are computed and mapped to categorical risk groups (0, 1–100, 101–300, >300). These standardized outputs enable cohort stratification, longitudinal analyses, survival modeling, and reproducible imaging biomarker research at scale [1].",
+      "references": [
+        {
+          "label": "DeepCAC2 preprint",
+          "uri": "https://arxiv.org/abs/2601.10154"
+        }
+      ],
+      "tables": [
+        {
+          "label": "CAC risk groups",
+          "entries": {
+            "0": "CACS = 0 (very low)",
+            "1": "CACS 1–100 (low)",
+            "2": "CACS 101–300 (moderate)",
+            "3": "CACS >300 (high)"
+          }
+        }
+      ]
+    },
+    "evaluation": {
+      "title": "Evaluation",
+      "text": "DeepCAC2 was evaluated for both technical validity and clinical relevance. Technical validation compared automated CAC scores and risk categories against expert annotations on a subset of NLST scans, demonstrating high agreement. Clinical validation using survival analysis showed that model-derived CAC risk groups are strong independent predictors of all-cause mortality, with clear stratification across risk categories [1].",
+      "references": [
+        {
+          "label": "DeepCAC2 preprint",
+          "uri": "https://arxiv.org/abs/2601.10154"
+        }
+      ],
+      "tables": [
+        {
+          "label": "Technical validation metrics",
+          "entries": {
+            "Evaluation dataset": "NLST subset",
+            "Sample size": "390 CT scans",
+            "Spearman correlation (CACS)": "0.879",
+            "Weighted Cohen's kappa (risk groups)": "0.844",
+            "Agreement interpretation": "High correlation and near-perfect agreement"
+          }
+        },
+        {
+          "label": "Survival analysis summary",
+          "entries": {
+            "Cohort size": "23,011 participants",
+            "Events": "1,624 deaths",
+            "Median follow-up": "6.7 years",
+            "Model type": "Cox proportional hazards",
+            "Adjusted covariates": "Age, sex, smoking status, heart disease",
+            "Highest risk group HR": "2.05 (95% CI 1.77–2.39)",
+            "Concordance index": "0.685"
+          }
+        }
+      ]
+    },
+    "training": {
+      "title": "Training",
+      "text": "The CAC segmentation model was trained in a supervised manner using 778 ECG-gated chest CT scans from the Framingham Heart Study with expert CAC annotations. The pipeline uses a two-stage approach: nnU-Net-based cardiac segmentation followed by patch-based 3D U-Net training on balanced overlapping 3D patches extracted from heart-centered volumes [1].",
+      "references": [
+        {
+          "label": "DeepCAC2 preprint",
+          "uri": "https://arxiv.org/abs/2601.10154"
+        }
+      ]
+    },
+    "limitations": {
+      "title": "Limitations",
+      "text": "The model is optimized for non-contrast, low-dose chest CT scans and may be sensitive to variations in acquisition protocols, reconstruction parameters, and image quality. CAC quantification is derived from non-ECG-gated scans, which may introduce motion-related inaccuracies compared to dedicated cardiac CT. Generalization to other populations, scanners, and clinical workflows requires further validation [1].",
+      "references": [
+        {
+          "label": "DeepCAC2 preprint",
+          "uri": "https://arxiv.org/abs/2601.10154"
+        }
+      ]
+    }
+  }
+}

models/deepcac2/mhub.toml

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+[model.deployment]
+test = "https://zenodo.org/records/19921552/files/mhub_test_deepcac2.zip"

models/deepcac2/src/.gitignore

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+weights/