Computed Tomography Brain Images Semantic Segmentation

Advances in Multimedia and Interactive Technologies - Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing ◽

10.4018/978-1-5225-2848-7.ch004 ◽

2017 ◽

pp. 77-104

Author(s):

Poonam Fauzdar ◽

Sarvesh Kumar

Keyword(s):

Computed Tomography ◽

Positive Predictive Value ◽

Euclidean Distance ◽

Global Analysis ◽

Semantic Segmentation ◽

Brain Images ◽

Minimum Euclidean Distance ◽

Computed Tomography Images ◽

Multi Level ◽

Reduced Time

In this paper we applianced an approach for segmenting brain tumour regions in a computed tomography images by proposing a multi-level fuzzy technique with quantization and minimum computed Euclidean distance applied to morphologically divided skull part. Since the edges identified with closed contours and further improved by adding minimum Euclidean distance, that is why the numerous results that are analyzed are very assuring and algorithm poses following advantages like less cost, global analysis of image, reduced time, more specificity and positive predictive value.

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Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

Journal of Physics Conference Series ◽

10.1088/1742-6596/2099/1/012021 ◽

2021 ◽

Vol 2099 (1) ◽

pp. 012021

Author(s):

A V Dobshik ◽

A A Tulupov ◽

V B Berikov

Keyword(s):

Computed Tomography ◽

Network Architecture ◽

Semantic Segmentation ◽

Training Data ◽

Neural Network Architecture ◽

Brain Images ◽

Tomographic Images ◽

Computed Tomography Images ◽

Segmentation Accuracy ◽

Weakly Supervised

Abstract This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke in the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architecture with several modifications. Experiments on real computed tomography scans show that the proposed methods increase the segmentation accuracy.

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Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Computers Materials & Continua ◽

10.32604/cmc.2021.014199 ◽

2021 ◽

Vol 68 (2) ◽

pp. 2451-2467

Author(s):

Javaria Amin ◽

Muhammad Sharif ◽

Muhammad Almas Anjum ◽

Yunyoung Nam ◽

Seifedine Kadry ◽

...

Keyword(s):

Computed Tomography ◽

Three Dimensional ◽

Semantic Segmentation ◽

Computed Tomography Images

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U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images

IEEE Access ◽

10.1109/access.2019.2923760 ◽

2019 ◽

Vol 7 ◽

pp. 82867-82877 ◽

Cited By ~ 6

Author(s):

Shuchao Chen ◽

Han Yang ◽

Jiawen Fu ◽

Weijian Mei ◽

Shuai Ren ◽

...

Keyword(s):

Computed Tomography ◽

Esophageal Cancer ◽

Semantic Segmentation ◽

Computed Tomography Images

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Semantic Segmentation Using Deep Learning to Extract Total Extraocular Muscles and Optic Nerve from Orbital Computed Tomography Images

Optik ◽

10.1016/j.ijleo.2021.167551 ◽

2021 ◽

pp. 167551

Author(s):

Fubao Zhu ◽

Zhengyuan Gao ◽

Chen Zhao ◽

Zelin Zhu ◽

Jinshan Tang ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Optic Nerve ◽

Semantic Segmentation ◽

Extraocular Muscles ◽

Computed Tomography Images

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Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery

Medical Physics ◽

10.1118/1.4818017 ◽

2013 ◽

Vol 40 (9) ◽

pp. 091912 ◽

Cited By ~ 4

Author(s):

Christina Stoecker ◽

Stefan Welter ◽

Jan H. Moltz ◽

Bianca Lassen ◽

Jan-Martin Kuhnigk ◽

...

Keyword(s):

Computed Tomography ◽

Pulmonary Artery ◽

Euclidean Distance ◽

Computed Tomography Images

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Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18126499 ◽

2021 ◽

Vol 18 (12) ◽

pp. 6499

Author(s):

Vidhya V. ◽

Anjan Gudigar ◽

U. Raghavendra ◽

Ajay Hegde ◽

Girish R. Menon ◽

...

Keyword(s):

Computed Tomography ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Future Research ◽

Brain Images ◽

Early Management ◽

Cad Systems ◽

Computed Tomography Images ◽

Secondary Injuries ◽

External Forces

Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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