Building Information Modelling (BIM) is a process that contain all the necessary information to manage the construction project across all its lifecycle. This benefits not only the construction industry but other indu...
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ISBN:
(纸本)9781665444071
Building Information Modelling (BIM) is a process that contain all the necessary information to manage the construction project across all its lifecycle. This benefits not only the construction industry but other industries such as utility companies that need to perform tasks inside of buildings and will need to access information about its elements. However, one of the biggest challenges is the digitalisation of the existing infrastructure. The use of semantic segmentation techniques could enable the transformation of infrastructure legacy data, such as 2D floor plans images, to open-standard BIM models. In this paper, we propose a processing pipeline to transform 2D floor plan images into BIM models. The pipeline makes use of an interval Type-2 Fuzzy Rule-based System (FRBS) that has an Intersection over Union metric value of 98.62% outperforming the Type-1 version of the model. Moreover, the proposed model is highly transparent, and it allows end-users to augment it using expert knowledge, something that is not possible in deep learning opaque-box models.
In this paper, we present a new tool for white matter lesion segmentation called lesionBrain. Our method is based on a 3-stage strategy including multimodal patch-based segmentation, patch-based regularization of prob...
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ISBN:
(纸本)9783030005009;9783030004996
In this paper, we present a new tool for white matter lesion segmentation called lesionBrain. Our method is based on a 3-stage strategy including multimodal patch-based segmentation, patch-based regularization of probability map and patch-based error correction using an ensemble of shallow neural networks. Its robustness and accuracy have been evaluated on the MSSEG challenge 2016 datasets. During our validation, the performance obtained by lesionBrain was competitive compared to recent deep learning methods. Moreover, lesionBrain proposes automatic lesion categorization according to location. Finally, complementary information on gray matter atrophy is included in the generated report. LesionBrain follows a software as a service model in full open access.
Cervical cancer is the second most common cause of death among women worldwide, but it can be treated if detected early. However, due to inter and intra observer variability in manual screening, automating the process...
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ISBN:
(数字)9781510616523
ISBN:
(纸本)9781510616523
Cervical cancer is the second most common cause of death among women worldwide, but it can be treated if detected early. However, due to inter and intra observer variability in manual screening, automating the process is need of the hour. For classifying the cervical cells as normal vs abnormal, segmentation of nuclei as well as cytoplasm is a prerequisite. But the segmentation of nuclei is relatively more reliable and equally efficient for classification to that of cytoplasm. Hence, this paper proposes a new approach for segmentation of nuclei based on selective pre-processing and then passing the image patches to respective deep CNN (trained with/without pre-processed images) for pixel wise 3 class labelling as nucleus, edge or background. We argue and demonstrate that a single pre-processing approach may not suit all images, as there are significant variations in nucleus sizes and chromatin patterns. The selective pre-processing is carried out to effectively address this issue. This also enables the deep CNNs to be better trained in spite of relatively less data, and thus better exploit the capability of CNN of good quality segmentation. The results show that the approach is effective for segmentation of nuclei in PAP-smears with an F-score of 0.90 on Herlev dataset as opposed to the without selective pre-processing F-scores of 0.78 (without pre-processing) and 0.82 (with pre-processing). The results also show the importance of considering 3 classes in CNN instead of 2 (nucleus and background) where the latter achieves an F-score as low as 0.63.
A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and s...
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A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.
Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in c...
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Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-basedsegmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-basedsegmentation, and the long training and the time consuming phase of learning methods, we proposed a semisupervised learning framework by introducing a probabilistic graph based method. It combines the advantages of label propagation and patch-based segmentation on a parametric graph. To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low and high-grade glioma tumors. The experimental results show that the proposed framework has accurate segmentation results. Compared with the state-of-the-art methods, the proposed framework could obtain the best dice score for segmenting the "whole tumor" (WT) and "tumor core" (TC) regions. The segmentation result of the "enhancing active tumor" (ET) region is similar to the most recent works compared in this paper.
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