README.md

metadata

title: Ct App
emoji: 🐠
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.18.0
python_version: '3.13'
app_file: app.py
pinned: false
license: apache-2.0
short_description: App that scans CT docs using TotalSegmentator model, flags o
tags:
  - track:backyard
  - sponsor:modal
  - achievement:offgrid
  - achievement:offbrand
  - achievement:fieldnotes

🩻 CT Report Generator

An automated 3D volumetric reporting pipeline for CT scans, powered by TotalSegmentator (3D U-Net) (⚡ ~30 Million Total Parameters) — deployed serverlessly on Modal.

📺 Watch the full video demo and post on X (Twitter)!

Special Bonus Targets : Tiny Titan (~30M parameters model) · Off-Brand Award

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📖 Overview

CT report generator is a Gradio-based clinical dashboard that automates the extraction and quantification of anatomical structures from 3D CT scans. It processes raw .nii / .nii.gz volumetric data, calculates the exact volume of dozens of internal organs, and automatically flags any measurements that fall outside of expected healthy reference ranges (e.g., hepatomegaly, splenomegaly, or asymmetrical kidneys).

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🚀 Features

Feature	Description
🧠 Total Body Segmentation	Automatically identifies and segments major solid organs, thoracic structures, and GI/GU tracts.
📊 Clinical Volume Alerts	Cross-references organ volumes with normal adult reference ranges and flags anomalies (e.g. Enlarged liver, asymmetric lungs).
🖼️ Cross-Section Preview	Generates an immediate mid-axial visual slice of the uploaded 3D volume.
📑 PDF Report Generation	Automatically compiles the findings into a clean, professional, downloadable PDF clinical report using WeasyPrint.

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🤖 AI Models Used

1. TotalSegmentator — 3D Anatomical Segmentation

• Architecture: 3D U-Net (nnU-Net framework)
• Total Parameters: ~30 Million (30M)
• Task: 3D medical image segmentation.
• Used for: Identifying and calculating the exact cubic centimeter (cm³) volume of 100+ anatomical structures from raw CT scans.
• Inference: Fast-mode enabled for rapid screening on Modal A10G GPU.

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🏗️ Architecture

GRADIO FRONTEND (app.py)
  ├── 3D Visualization     → nibabel + PIL (Mid-axial slice rendering)
  ├── Validation           → Checks for valid 3D shape and intensity spread
  ├── PDF Generation       → WeasyPrint HTML-to-PDF conversion
  └── Remote RPC           → Connects to Modal backend via `modal.Cls`

MODAL SERVERLESS BACKEND (backend.py)
  └── Segmenter [A10G]     → `TotalSegmentator` subprocess
                           → JSON parsing & Reference Range Logic
                           → Returns structured clinical findings

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🖥️ GPU Resources (Modal)

Container	GPU	Model(s)	Purpose
Segmenter	A10G (24GB)	TotalSegmentator 3D U-Net	Heavy volumetric segmentation and pixel quantification

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⚙️ Setup & Deployment

# 1. Install dependencies
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# 2. Deploy Modal backend
modal deploy backend.py

# 3. Run the Gradio frontend
python app.py