Automated Multiple Sclerosis Lesion Segmentation on MR Images via Mask R-CNN

Yildirim, Mehmet Suleyman; Dandıl, Emre

doi:10.1109/ismsit52890.2021.9604593

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Ansari et al 5 proposed a CNN module with inception blocks adapted from the GoogLeNet model and optimized using Binary Cross Entropy (BCE) Loss and Structural Similarity Index Measure (SSIM) Loss. Yıldırım etal 23 . proposed using the Mask R‐CNN architecture introduced by He et al 38 for specific MS lesion segmentation tasks.…”

Section: Related Workmentioning

confidence: 99%

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Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified U‐Net model

Amaludin

Kadry²,

Ting

et al. 2023

Int J Imaging Syst Tech

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Early diagnosis of multiple sclerosis (MS) through the delineation of lesions in the brain magnetic resonance imaging is important in preventing the deteriorating condition of MS. This study aims to develop a modified U‐Net model for automating lesions segmentation in MS more accurately. The proposed modified U‐Net uses residual dense blocks to replace the standard convolutional stacks and incorporates three axes (axial, sagittal, and coronal) of 2D slice images as input. Furthermore, a custom fusion method is also introduced for merging the predicted lesions from different axes. The model was implemented on ISBI2015 and OpenMS data sets. On ISBI2015, the proposed model achieves the best overall score of 93.090% and DSC of 0.857 on the OpenMS data set.

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Section: Related Workmentioning

confidence: 99%

“…Yıldırım etal. 23 proposed using the Mask R-CNN architecture introduced by He et al 38 for specific MS lesion segmentation tasks. They used ResNet50 and ResNet101 as the backbone of their Mask R-CNN architecture.…”

Section: Related Workmentioning

confidence: 99%