MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

Abstract

The 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. 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. Running a crossvalidation 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. With 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.

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

“…To segment the COVID-19 infection from lung CT images, LungINFseg is compared with the state-of-the-art segmentation models, such as FCN [ 15 ], UNet [ 16 ], SegNet [ 17 ], FSSNet [ 18 ], SQNet [ 19 ], ContextNet [ 20 ], EDANet [ 21 ], CGNet [ 22 ], ERFNet [ 23 ], ESNet [ 24 ], DABNet [ 25 ], Inf-Net [ 12 ], and MIScnn [ 26 ] models. All these models are assessed both quantitatively and qualitatively.…”

“…Additionally, we have trained MIScnn [ 26 ] from scratch and then compared it with LungINFseg, finding that our model outperforms the results of MIScnn in terms of all evaluation metrics. Unlike the models mentioned-above, LungINFseg has a great generalization ability to segment the infection areas from lung CT images, thanks to RFA and DWT modules that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss.…”

“… We propose the RFA module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. We present a comprehensive comparison with 13 state-of-the-art segmentation models, namely, FCN [ 15 ], UNet [ 16 ], SegNet [ 17 ], FSSNet [ 18 ], SQNet [ 19 ], ContextNet [ 20 ], EDANet [ 21 ], CGNet [ 22 ], ERFNet [ 23 ], ESNet [ 24 ], DABNet [ 25 ], Inf-Net [ 12 ], and MIScnn [ 26 ]. Extensive experiments were performed to provide ablation studies that add a thorough analysis of the proposed LungINFSeg (e.g., the effect of resolution size and variation of the loss function).…”

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

“…For our experiments, we use the Medical Imaging Segmentation with Convolutional Neural Networks (MIScnn) open-source Python library [ 68 ]. For all datasets, images and associated ground truth masks are provided in the png file format.…”

COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning

“…The pipeline was based on our in-house developed framework MIScnn [2]. We utilized the COVID-19 Lung CT Lesion Segmentation Challenge 2020 dataset containing 245 CT scans into a pre-defined training and testing dataset consisting of 199 and 46 samples, respectively 2 .…”