Medical Image Processing and Machine Learning Research

Clinically acquired new challenging dataset for brain SOL segmentation: AJBDS-2023 Space-occupying lesions (SOL) brain detected on brain MRI are benign and malignant tumors. Several brain tumor segmentation algorithms have been developed but there is a need for a clinically acquired dataset that is used for real-time images. This research is done to facilitate reporting of MRI done for brain tumor detection by incorporating computer-aided detection. Another objective was to make reporting unbiased by decreasing inter-observer errors and expediting daily reporting sessions to decrease radiologists’ workload. This is an experimental study. The proposed dataset contains clinically acquired multiplanar, multi-sequential MRI slices (MPMSI) which are used as input to the segmentation model without any preprocessing. The proposed AJBDS-2023 consists of 10667 images of real patients imaging data with a size of 320*320*3. Acquired images have T1W, TW2, Flair, T1W contrast, ADC, and DWI sequences. Pixel-based ground-truth annotated images of the tumor core and edema of 6334 slices are made manually under the supervision of a radiologist. Quantitative assessment of AJBDS-2023 images is done by a novel U-network on 4333 MRI slices. The diagnostic accuracy of our algorithm U-Net trained on AJBDS-2023 was 77.4 precision, 82.3 DSC, 87.4 specificity, 93.8 sensitivity, and 90.4 confidence interval. An experimental analysis of AJBDS-2023 done by the U-Net segmentation model proves that the proposed AJBDS-2023 dataset has images without preprocessing, which is more challenging and provides a more realistic platform for evaluation and analysis of newly developed algorithms in this domain and helps radiologists in MRI brain reporting more realistically.

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Please cite these paper when using this dataset 1. Amin, J., Anjum, M. A., Gul, N., Sharif, M., & Kadry, S. (2023). Clinically Acquired New Challenging Dataset for Brain SOL Segmentation: AJBDS-2023. Data in Brief, 109915. 2. Amin, Javaria, Muhammad Almas Anjum, Nadia Gul, and Muhammad Sharif. "A secure two-qubit quantum model for segmentation and classification of brain tumor using MRI images based on blockchain." Neural Computing and Applications (2022): 1-14. 3. Amin, Javaria, Muhammad Almas Anjum, Nadia Gul, and Muhammad Sharif. "Detection of brain space-occupying lesions using quantum machine learning." Neural Computing and Applications (2023): 1-17.