Reverse Dependencies of dicom2nifti

The following projects have a declared dependency on dicom2nifti:

• altadb — AltaDB platform Python SDK!
• CartiMorph-nnUnet — nnU-Net with minor revisions tailored for the CartiMorph framework.
• cerebrumscanner — Cerebrum Scanner Project
• CTlessPET — CTlessPET for synthetic CT from NAC PET data
• dcmpi — DICOM Preprocessing Interface.
• dicomthings — A Python library for working with DICOM files.
• dsbundle — Streamline your data science setup with dsbundle in one effortless install.
• easy-mitk — An easy-to-use Medical Imaging ToolKit
• falconz — FalconZ: A streamlined Python package for PET motion correction.
• fmri-anonymizer — Anonymize your DICOM and NIFTI files with this tool easily.
• Hive-MAIA — Python Package to support Deep Learning data preparation, pre-processing. training, result visualization and model deployment across different frameworks (nnUNet, nnDetection, MONAI).
• ifree — i love freedom, free my hand.
• imvis — Interactive visualization and processing of 3D medical images in python
• lionz — A toolkit for precise segmentation of tumors in PET/CT scans.
• mdai — MD.ai Python client library
• mircat-stats — Mirshahi CT Analysis Toolkit (MirCAT), stats and dicom conversion only. Convert and Quantify CT NIfTI files.
• mircato — Add your description here
• mircato-stats — Statistics only version of MirCATo for CT image analysis. Much smaller build without torch.
• moosez — An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks
• mrdataset — MRdataset
• multi_med_image_ml — Deep learning library to encode multiple brain images and other electronic health record data in disease detection.
• nnunet — nnU-Net. Framework for out-of-the box biomedical image segmentation.
• nnunet-customized — nnU-Net. Framework for out-of-the box biomedical image segmentation.
• nnunet-inference-on-cpu-and-gpu — nnU-Net. Framework for out-of-the box biomedical image segmentation. Can do inference on both gpu(if cuda available) and cpu(if cuda not available)
• nnunetv2 — nnU-Net is a framework for out-of-the box image segmentation.
• nnunetv2-bm-custom — nnU-Net is a framework for out-of-the box image segmentation.
• nuclearowl — A lib handling nuclear imaging data and Ai applications on top of it
• ocelotz — OCELOT: diffeOmorphiC rEgistration for voxel-wise anOmaly Tracking - a tool to generate cohort specific normative PET/CT images.
• orcaz — ORCA (Optimized Registration through Conditional Adversarial networks)
• p-mod-api — Propelwise Modules
• p2022 — Propelwise Modules
• pattools — Toolkit for neuro-imaging data manipulation and automation
• pinr — Python tools for Interoperable Neuromorphometry Reporting
• pumaz — PUMA (PET Universal Multi-tracer Aligner) is a robust and efficient tool for aligning images from different PET tracers. It leverages advanced diffeomorphic imaging techniques to offer high-precision alignment for multiplexed tracer images. PUMA aims to significantly enhance the accuracy and reproducibility of PET image studies.
• pydactim — A library to post-process MRI data
• pymaia-learn — Python Package to support Deep Learning data preparation, pre-processing. training, result visualization and model deployment across different frameworks (nnUNet, nnDetection, MONAI).
• raymics — Raymics Tools
• redbrick-sdk — RedBrick platform Python SDK!
• SkinSegmentator — Robust segmentation of skin surface in MR images.
• tcsa — temporalis segmentation pipeline to assess CSA of temporalis muscle
• TotalSegmentator — Robust segmentation of 104 classes in CT images.
• UnPAC — no summary
• xnatum — A package for connecting and manage data on XNAT.