T1Prep

v0.4.5 safe
3.0
Low Risk

T1 PREProcessing Pipeline (aka PyCAT)

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscation. The metadata risk slightly elevates the concern due to the maintainer's account status, but overall, there is insufficient evidence to suggest a supply-chain attack.

  • No network calls detected
  • Maintainer has a new or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no direct command execution risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity aimed at stealing credentials.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which may indicate low credibility but does not strongly suggest malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: uni-jena.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ChristianGaser/T1Prep appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with T1Prep 
Create a medical imaging preprocessing tool using the T1Prep Python package. This tool will streamline the process of preparing MRI T1-weighted images for further analysis, such as segmentation or feature extraction. The application should include the following functionalities:

1. **Image Importation**: Allow users to import T1-weighted MRI images in standard formats like DICOM or NIFTI.
2. **Basic Preprocessing Steps**: Implement basic preprocessing steps provided by T1Prep, including bias field correction, skull stripping, and intensity normalization.
3. **Visualization**: Integrate visualization capabilities to display the original image and the processed image side by side, highlighting changes made during preprocessing.
4. **Parameter Tuning**: Enable users to adjust parameters for each preprocessing step, allowing them to fine-tune the results according to their specific needs.
5. **Output Export**: Provide options for exporting the preprocessed image in various formats, ensuring compatibility with different downstream analysis tools.
6. **Documentation and Help**: Include comprehensive documentation and help sections within the application to guide users through the preprocessing workflow and parameter settings.

The application should leverage T1Prep's core features to ensure high-quality preprocessing outcomes. Users should be able to understand the impact of each preprocessing step on the final image quality and learn how to optimize these steps for their specific use case.