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Segmentation of bone structures with the use of deep learning techniques

Zuzanna Krawczyk Jacek Starzyński
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e136751 | DOI: 10.24425/bpasts.2021.136751
Keywords deep learning semantic segmentation U-net FCN ResNet computed tomography
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Abstract

The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
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Authors and Affiliations

Zuzanna Krawczyk
1
Jacek Starzyński
1
  1. Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland

U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images

Tomasz Les
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137051 | DOI: 10.24425/bpasts.2021.137051
Keywords kidney detection medical image processing U-Net frames partitioning volumetric analysis
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Abstract

This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.
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Authors and Affiliations

Tomasz Les
1
  1. Faculty of Electrical Engineering, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland

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