---
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)!](https://x.com/AKIS23820044161/status/2066586748541657272)**

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

## 📖 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).

---

## 🚀 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. |

---

## 🤖 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**.

---

## 🏗️ 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
```

---

## 🖥️ GPU Resources (Modal)

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

---

## ⚙️ Setup & Deployment

```bash
# 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
```