| 1. |
- Agarwala, Sunita, et al.
(author)
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Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images
- 2018
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In: Medical Image Understanding and Analysis. - Cham : Springer Nature. - 9783319959214 - 9783319959207 ; , s. 12-22
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Conference paper (peer-reviewed)abstract
- Lung field segmentation is the first step towards the development of any computer aided diagnosis (CAD) system for interstitial lung diseases (ILD) observed in chest high resolution computed tomography (HRCT) images. If the segmentation is not done efficiently it will compromise the accuracy of CAD system. In this paper, a deep learning-based method is proposed to localize several interstitial lung disease patterns (ILD) in HRCT images without performing lung field segmentation. In this paper, localization of several ILD patterns is performed in image slice. The pretrained models of ZF and VGG networks were fine-tuned in order to localize ILD patterns using Faster R-CNN framework. The three most difficult ILD patterns consolidation, emphysema, and fibrosis have been used for this study and the accuracy of the method has been evaluated in terms of mean average precision (mAP) and free receiver operating characteristic (FROC) curve. The model achieved mAP value of 75% and 83% on ZF and VGG networks, respectively. The result obtained shows the effectiveness of the method in the localization of different ILD patterns.
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| 2. |
- Blache, Ludovic, et al.
(author)
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SoftCut: : A Virtual Planning Tool for Soft Tissue Resection on CT Images
- 2018
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In: Medical Image Understanding and Analysis. - Cham : Springer. - 9783319959207 ; , s. 299-310
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Conference paper (peer-reviewed)abstract
- With the increasing use of three-dimensional (3D) models and Computer Aided Design (CAD) in the medical domain, virtual surgical planning is now frequently used. Most of the current solutions focus on bone surgical operations. However, for head and neck oncologic resection, soft tissue ablation and reconstruction are common operations. In this paper, we propose a method to provide a fast and efficient estimation of shape and dimensions of soft tissue resections. Our approach takes advantage of a simple sketch-based interface which allows the user to paint the contour of the resection on a patient specific 3D model reconstructed from a computed tomography (CT) scan. The volume is then virtually cut and carved following this pattern. From the outline of the resection defined on the skin surface as a closed curve, we can identify which areas of the skin are inside or outside this shape. We then use distance transforms to identify the soft tissue voxels which are closer from the inside of this shape. Thus, we can propagate the shape of the resection inside the soft tissue layers of the volume. We demonstrate the usefulness of the method on patient specific CT data.
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| 3. |
- Kumar, Abhishek, et al.
(author)
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Segmentation of Lung Field in HRCT Images Using U-Net Based Fully Convolutional Networks
- 2018
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In: Medical Image Understanding and Analysis. - Cham : Springer Nature. - 9783319959214 - 9783319959207 ; , s. 84-93
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Conference paper (peer-reviewed)abstract
- Segmentation is a preliminary step towards the development of automated computer aided diagnosis system (CAD). The system accuracy and efficiency primarily depend on the accurate segmentation result. Effective lung field segmentation is major challenging task, especially in the presence of different types of interstitial lung diseases (ILD). At present, high resolution computed tomography (HRCT) is considered to be the best imaging modality to observe ILD patterns. The most common patterns based on their textural appearances are consolidation, emphysema, fibrosis, ground glass opacity (GGO), reticulation and micronodules. In this paper, automatic lung field segmentation of pathological lung has been done using U-Net based deep convolutional networks. Our proposed model has been evaluated on publicly available MedGIFT database. The segmentation result was evaluated in terms of the dice similarity coefficient (DSC). Finally, the experimental results obtained on 330 testing images of different patterns achieving 94% of average DSC.
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| 4. |
- Lennartsson, Finn, et al.
(author)
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Developing a framework for studying brain networks in neonatal hypoxic-ischemic encephalopathy
- 2018
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In: Medical Image Understanding and Analysis - 22nd Conference, Proceedings. - Cham : Springer International Publishing. - 1865-0929. - 9783319959207 ; 894, s. 203-216
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Conference paper (peer-reviewed)abstract
- Newborns with hypoxic-ischemic encephalopathy (HIE) are at high risk of brain injury, with subsequent developmental problems including severe neuromotor, cognitive and behavioral impairment. Neural correlates of cognitive and behavioral impairment in neonatal HIE, in particular in infants who survive without severe neuromotor impairment, are poorly understood. It is reasonable to hypothesize that in HIE both structural and functional brain networks are altered, and that this might be the neural correlate of impaired cognitive and/or behavioral impairment in HIE. Here, an analysis pipeline to study the structural and functional brain networks from neonatal MRI in newborns with HIE is presented. The structural connectivity is generated from dense whole-brain tractograms derived from diffusion-weighted MR fibre tractography. This investigation of functional connectivity focuses on the emerging resting state networks (RSNs), which are sensitive to injuries from hypoxic-ischemic insults to the newborn brain. In conjunction with the structural connectivity, alterations to the structuro-functional connectivity of the RSNs can be studied. Preliminary results from a proof-of-concept study in a small cohort of newborns with HIE are promising. The obstacles encountered and improvements to the pipeline are discussed. The framework can be further extended for joint analysis with EEG functional-connectivity.
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