Enhanced CATBraTS for Brain Tumour Semantic Segmentation
The early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/1/8 |
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| author | Rim El Badaoui Ester Bonmati Coll Alexandra Psarrou Hykoush A. Asaturyan Barbara Villarini |
| author_facet | Rim El Badaoui Ester Bonmati Coll Alexandra Psarrou Hykoush A. Asaturyan Barbara Villarini |
| author_sort | Rim El Badaoui |
| collection | DOAJ |
| description | The early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation. |
| format | Article |
| id | doaj-art-279fc51c231f423cb0bd8bc9063945c1 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-279fc51c231f423cb0bd8bc9063945c12025-01-24T13:36:15ZengMDPI AGJournal of Imaging2313-433X2025-01-01111810.3390/jimaging11010008Enhanced CATBraTS for Brain Tumour Semantic SegmentationRim El Badaoui0Ester Bonmati Coll1Alexandra Psarrou2Hykoush A. Asaturyan3Barbara Villarini4School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKSchool of Computer Science and Engineering, University of Westminster, London W1W 6UW, UKThe early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.https://www.mdpi.com/2313-433X/11/1/8brain tumourconvolutional neural networksemantic segmentationtransformertumour segmentation |
| spellingShingle | Rim El Badaoui Ester Bonmati Coll Alexandra Psarrou Hykoush A. Asaturyan Barbara Villarini Enhanced CATBraTS for Brain Tumour Semantic Segmentation Journal of Imaging brain tumour convolutional neural network semantic segmentation transformer tumour segmentation |
| title | Enhanced CATBraTS for Brain Tumour Semantic Segmentation |
| title_full | Enhanced CATBraTS for Brain Tumour Semantic Segmentation |
| title_fullStr | Enhanced CATBraTS for Brain Tumour Semantic Segmentation |
| title_full_unstemmed | Enhanced CATBraTS for Brain Tumour Semantic Segmentation |
| title_short | Enhanced CATBraTS for Brain Tumour Semantic Segmentation |
| title_sort | enhanced catbrats for brain tumour semantic segmentation |
| topic | brain tumour convolutional neural network semantic segmentation transformer tumour segmentation |
| url | https://www.mdpi.com/2313-433X/11/1/8 |
| work_keys_str_mv | AT rimelbadaoui enhancedcatbratsforbraintumoursemanticsegmentation AT esterbonmaticoll enhancedcatbratsforbraintumoursemanticsegmentation AT alexandrapsarrou enhancedcatbratsforbraintumoursemanticsegmentation AT hykoushaasaturyan enhancedcatbratsforbraintumoursemanticsegmentation AT barbaravillarini enhancedcatbratsforbraintumoursemanticsegmentation |