ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine

Yiqin Zhang; Meiling Chen

cs.SEMar 15, 2026

ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine

Authors: Yiqin Zhang, Meiling Chen

Abstract

CT images are widely used in clinical diagnosis and treatment, and their data have formed a de facto standard - DICOM. It is clear and easy to use, and can be efficiently utilized by data-driven analysis methods such as deep learning. In the past decade, many program frameworks for medical image analysis have emerged in the open-source community. ITKIT analyzed the characteristics of these frameworks and hopes to provide a better choice in terms of ease of use and configurability. ITKIT offers a complete pipeline from DICOM to 3D segmentation inference. Its basic practice only includes some essential steps, enabling users with relatively weak computing capabilities to quickly get started using the CLI according to the documentation. For advanced users, the OneDL-MMEngine framework provides a flexible model configuration and deployment entry. This paper conducted 12 typical experiments to verify that ITKIT can meet the needs of most basic scenarios.

Figures & tables

Project	Stars a	Data Model		Preprocessing		Deep-Learning		Evaluation
Project	Stars a	Integrated	CLI	Integrated	CLI	Integrated	CLI	Integrated	CLI
MONAI [ 2 ]	7632	✓	✓	✓		✓	✓	✓
ITK [ 25 , 40 ] /S.ITK [ 20 ]	1556/1028	✓		✓
Dipy [ 7 ]	795	✓	✓	✓	✓	✓	✓	✓	✓
nnUNet [ 12 ]	7788	✓	✓	✓	✓	✓	✓	✓	✓
OpenMedIA	N/A			✓		✓		✓

Table 1: Comparison of ITKIT with Other Existing Projects

Figure 1: Recommended Dataset Structure for ITKIT. The image and label folders contain the volumetric data and corresponding semantic masks, respectively. The meta.json file stores metadata for the entire dataset, while crop_meta.json contains metadata for any cropped or patched sub-volumes derived from the original data.

Figure 2: Scope of ITKIT’s Functional Abstractions

Dataset Name	Number of Classes a	Modality
AbdomenCT-1K [ 24 ]	5	CT
CT-ORG [ 29 ]	6	CT
CT-Spine1K [ 3 ]	26	CT
FLARE 2022 [ 22 ]	14	CT
FLARE 2023 [ 23 ]	15	CT
Image-TBAD [ 39 ]	4	CT

Table 2: Supported Public Datasets in ITKIT.

Model	Year a	Architecture	Dimension b	Install & Run
UNet 3+ [ 11 ]	2020	CNN	2D/3D	✓
EfficientNet [ 14 ]	2021	CNN	2D	✓
SegFormer [ 36 ]	2021	Transformer	2D	Requires Official Repo
DSNet [ 28 ]	2021	CNN	2D	✓
UNETR [ 8 ]	2022	Transformer	3D	✓
EfficientFormer [ 16 ]	2022	Transformer	2D	✓

Table 3: Integrated Neural Networks in ITKIT.

Panel of Figure 3 — Figure 3: ITKIT 3D Slicer Extension Screenshot. The left side displays the ITKIT extension, providing an interface for configuring the parameters required to perform segmentation inference. The right side shows the results sematic mask of spleen, generated using an ONNX model trained and converted by ITKIT. In this case, the segment engine runs in a Docker container under WSL on Windows.

Task	Data Backend	Model	Duration(s)	Dice	IoU	Recall	Precision
AbdomenCT1K	Native	MedNeXt	119039	83.41	75.98	83.43	83.69
	Native	SegFormer3D	17493	87.25	79.84	84.53	90.31
	TorchIO	MedNeXt	135806	88.25	80.88	85.94	91.36
	TorchIO	SegFormer3D	25226	94.08	89.50	94.91	93.29
	MONAI	MedNeXt	84437	88.66	81.69	85.92	92.41
	MONAI	SegFormer3D	24037	89.88	83.27	86.76	93.54

Table 4: Baseline Segmentation Results.

Figure 5: ITKIT Visualization. The image is saved to both local files and TensorBoard during training.

Medical Image Analysis

Medical image analysis uses computational methods to interpret clinical images, measure anatomy and disease, and support diagnosis and treatment.

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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms

Juntao Jiang, Jinsheng Bai, Linxuan Fan, Yali Bi+2

Zhejiang University, APRIL Lab · Vanderbilt University · University of Science and Technology of China

Medical Image Segmentation

Open paper AskPDF arXiv

We present APRIL-MedSeg, a YAML-driven modular framework for 2D medical image segmentation. It provides a unified and extensible ecosystem that decomposes segmentation networks into reusable components. Also, the framework integrates a broad spectrum of advanced paradigms, including semi-supervised learning, domain adaptation, knowledge distillation, weakly supervised learning, and text-guided segmentation as well as foundation model support. A registry-based configuration system with inheritance enables flexible and reproducible experiment management, supporting seamless switching across models, datasets, and training strategies. In addition, the framework provides a unified interface for medical datasets, augmentation pipelines, deployment utilities and model ensembling. Overall, APRIL-MedSeg is designed as a general-purpose research and development platform that bridges algorithmic innovation and practical deployment, while also serving as a structured ecosystem for systematically organizing and reproducing advances in medical image segmentation. The code is available at https://github.com/juntaoJianggavin/APRIL-MedSeg under an Apache 2.0 license.

Abstract

Medical Image Analysis

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