SegNet-based Corpus Callosum segmentation for brain Magnetic Resonance Images (MRI)

Chandra, Anjali; Verma, Shrish; Raghuvanshi, Ajay Singh; Bodhey, Narendra Kuber; Londhe, Narendra D.; Kumar, Surender

doi:10.1109/icccis48478.2019.8974559

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…DeepLab V3+ delivers fewer extracted feature types from the encoder than the proposed NN, resulting in a rough segmentation boundary from information loss. Although SegNet was initially developed for road scene images, many variants are certified for medical imaging [ 31 – 34 ]. We used the parameter settings for SegNet suggested by Chandra et al [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…Although SegNet was initially developed for road scene images, many variants are certified for medical imaging [ 31 – 34 ]. We used the parameter settings for SegNet suggested by Chandra et al [ 34 ]. Contrasting the proposed NN, SegNet does not exploit additional encoder information except for max pooling indexes during the decoding process.…”

Section: Methodsmentioning

confidence: 99%

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Effective encoder-decoder neural network for segmentation of orbital tissue in computed tomography images of Graves’ orbitopathy patients

Lee

et al. 2023

PLoS ONE

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Purpose To propose a neural network (NN) that can effectively segment orbital tissue in computed tomography (CT) images of Graves’ orbitopathy (GO) patients. Methods We analyzed orbital CT scans from 701 GO patients diagnosed between 2010 and 2019 and devised an effective NN specializing in semantic orbital tissue segmentation in GO patients’ CT images. After four conventional (Attention U-Net, DeepLab V3+, SegNet, and HarDNet-MSEG) and the proposed NN train the various manual orbital tissue segmentations, we calculated the Dice coefficient and Intersection over Union for comparison. Results CT images of the eyeball, four rectus muscles, the optic nerve, and the lacrimal gland tissues from all 701 patients were analyzed in this study. In the axial image with the largest eyeball area, the proposed NN achieved the best performance, with Dice coefficients of 98.2% for the eyeball, 94.1% for the optic nerve, 93.0% for the medial rectus muscle, and 91.1% for the lateral rectus muscle. The proposed NN also gave the best performance for the coronal image. Our qualitative analysis demonstrated that the proposed NN outputs provided more sophisticated orbital tissue segmentations for GO patients than the conventional NNs. Conclusion We concluded that our proposed NN exhibited an improved CT image segmentation for GO patients over conventional NNs designed for semantic segmentation tasks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%