Reverse Dependencies of dicom2nifti

The following projects have a declared dependency on dicom2nifti:

• CartiMorph-nnUnet — nnU-Net with minor revisions tailored for the CartiMorph framework.
• CTlessPET — CTlessPET for synthetic CT from NAC PET data
• DBSegment — A deep learnign based method to segment deep brain structures from T1w MRI
• dcmpi — DICOM Preprocessing Interface.
• dicomthings — A Python library for working with DICOM files.
• 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
• moosez — An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks
• mrdataset — MRdataset
• 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.
• 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 — no summary
• 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.
• TPTBox — A Torso Processing Toolbox capable of processing BIDS-compatible datasets, singular niftys, points of interests, segmentations, and much more.
• xnatum — A package for connecting and manage data on XNAT.