Clinically acquired new challenging dataset for brain SOL segmentation: AJBDS-2023

Abstract

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.

Keywords: Algorithm; Almas Javeria Brain dataset (AJBDS)-2023; Multiplanar multi-sequential images (MPMSI); Segmentation.

Fig. 1

Shows the labels (the blue region denotes the edema, and the red region represents the tumor.

Fig. 2

Design of the proposed study [SOL brain of axial, coronial and sagittal views images of AJBDS-23 dataset are passed to the proposed U-net model for segmentation].

Fig. 3

(A) input images (B) manual annotations by radiologists (C) binary segmentation [In which shows the labels (Row 1) axial view of T2 shows edema (blue), (Row 2) axial T2 Flair shows solid tumor component in red color, (Row 3) (T1 with contrast) red core tumor with necrotic cells and solid tumor (red) color, edema (blue), (Row 4), T1, axial view red shows the solid tumor].

Fig. 4

Segmentation results of the proposed method Column of (A) (C) sagittal, coronial, axial views and Column of (B) (D) segmented region [In which Row 1, Row 2, Row 3 shows Sagittal, Coronial and Axial respectively].