Data Augmentation Method for Deep Learning based Medical Image
                        Segmentation Model

Abstract Background 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. 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. Results Running 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. Conclusions 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.

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Efficient Medical Image Segmentation Of COVID-19 Chest CT Images Based on Deep Learning Techniques

2021 International Conference on Emerging Smart Computing and Informatics (ESCI) ◽

10.1109/esci50559.2021.9397043 ◽

2021 ◽

Author(s):

Sanika Walvekar ◽

Swati Shinde

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Ct Images ◽

Medical Image Segmentation ◽

Chest Ct ◽

Learning Techniques

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PO-1689 Sample pairing (mixup) as a data augmentation technique for deep medical image segmentation networks

Radiotherapy and Oncology ◽

10.1016/s0167-8140(21)08140-8 ◽

2021 ◽

Vol 161 ◽

pp. S1415-S1416

Author(s):

L.J. Isaksson

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Data Augmentation ◽

Medical Image Segmentation

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HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽

10.3390/sym13112107 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2107

Author(s):

Xin Wei ◽

Huan Wan ◽

Fanghua Ye ◽

Weidong Min

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Loss Function ◽

Network Structure ◽

Medical Diagnosis ◽

Medical Image ◽

Medical Image Segmentation ◽

Training Mode ◽

Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

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Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2022-0003 ◽

2021 ◽

Vol 12 (1) ◽

pp. 29-39

Author(s):

Lars J. Isaksson ◽

Paul Summers ◽

Sara Raimondi ◽

Sara Gandini ◽

Abhir Bhalerao ◽

...

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Data Augmentation ◽

Image Data ◽

Magnetic Resonance Images ◽

Medical Image Segmentation ◽

Dice Similarity Coefficient ◽

Surface Distance ◽

Experienced Radiologist ◽

Generalization Problem

Abstract Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.

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