Medical images stabilization using sparse-induced similarity measure

Hariri, Ali; Arabshahi, Soroush; Ghafari, Aboozar; Fatemizadeh, Emad

doi:10.1109/pria.2015.7161624

Cited by 1 publication

(1 citation statement)

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“…Produced in Figure 4a is the spatial distributions of each tumor class provided to QuickTumorNat rendered as a 3D image via manual traditional registration-the process of transforming different sets of data into one coordinate system [17]-with 9 degrees of freedom. Figure 4b is the spatial distribution of the sizes of each tumor class provided to our model.…”

Section: A Spatial Distribution and Sizes Of Tumor Classes In Data Setmentioning

confidence: 99%

QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors

Maas

Zabeh

Arabshahi

2021

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

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Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and reliable method for brain tumor detection and quantification. In this study, we propose an end-to-end approach for brain tumor segmentation, capitalizing on a modified version of QuickNAT, a brain tissue type segmentation deep convolutional neural network (CNN). Our method was evaluated on a data set of 233 patient's T1 weighted images containing three tumor type classes annotated (meningioma, glioma, and pituitary). Our model, QuickTumorNet, demonstrated fast, reliable, and accurate brain tumor segmentation that can be utilized to assist clinicians in diagnosis and treatment.

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Section: A Spatial Distribution and Sizes Of Tumor Classes In Data Setmentioning

confidence: 99%