ArticleBMC medical imaging2021
MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed.
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- Multi-interactive feature embedding learning for medical image segmentation.Frontiers in medicine · 2025Article
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- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.Journal of clinical medicine · 2024Article
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- Automation of Wilms' tumor segmentation by artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study.Journal of applied clinical medical physics · 2024Article
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2 authors.
Funding
Abstract
backgroundThe increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. IMPLEMENTATION: The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization.
resultsRunning a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model.
conclusionsWith this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn .
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