Fig 1.
AI network overview and AI network characterization.
A: Schematic representation of Faster R-CNN. B: Schematic representation of RetinaNet. C: Schematic representation of YOLOv3. D: Overview of the distribution of IBA1+ cells detected in the analysed images. E: Curve of mean Average Precision values in the test set versus epoch number. F: average time (s) for manual quantification, Faster R-CNN, RetinaNet and YOLOv3.
Table 1.
Overview of the images used for training dataset and validation dataset for the algorithms.
Table 2.
Overview of images used for robustness testing.
Table 3.
Overview of the quantified images and validation of the algorithms with other methods.
Fig 2.
Quantification analysis of IBA1+ cells in three different mammalian species.
A: Source image of rat SN immunolabelled for IBA1+ microglial cells. B: Closeup of image A. C-E: output of AI-based quantification using Faster R-CNN, RetinaNet, and YOLOv3, respectively. F: Source image of mouse SN immunolabelled for IBA1+ microglial cells. G: Closeup of image F. H-J: output of AI-based quantification using Faster R-CNN, RetinaNet and YOLOv3, respectively. K: Source image of NHP SN immunolabelled for IBA1+ microglial cells. L: Closeup of image K. M-O: output of AI-based quantification using Faster R-CNN, RetinaNet, and YOLOv3 respectively. For all images, each bounding box represents the detected, and therefore quantified cell. P-S: Comparison of the different quantification methods for rats injected with the control vector on the contralateral (P) and ipsilateral side of injection (Q) as well as for rats injected with the vector overexpressing h-aSYN on the contralateral (R) and ipsilateral (S) side of injection. T, U: Comparison of the different quantification methods for the IBA1+ cells in the SN of WT (T) and 5xFAD mice (U). V: Comparison of the different quantification methods for the IBA1+ cells in the SN of WT NHP. W: Comparison of the different quantification methods for the IBA1+ cells in all the three analysed species. Data are expressed as mean ± SEM; *** = p < 0.001.
Table 4.
Average Precision (AP) score at different IOU threshold.
Fig 3.
Correlation analysis of the different quantification methods.
Correlation analysis for the AAV-noTG and AAV-aSYN (both ipsilateral and contralateral hemisphere) groups comparing manual quantification vs A: Faster R-CNN; B: RetinaNet; C: YOLOv3; D: Ilastik; E: Fiji. Correlation analysis for the AAV-noTG, AAV-aSYN rat group (both ipsilateral and contralateral hemisphere), WT and 5xFAD mice groups and WT NHP group comparing manual quantification vs F: Faster R-CNN; G: RetinaNet; H: YOLOv3; I: Ilastik; J: Fiji.
Fig 4.
Robustness to images from other setups.
Iba1-DAB immunolabelled brightfield images from three different laboratories (University College Cork, UCC (A-C); Michigan State University, MSU (E-G) and Barrow Neurological, BN (I-K). A representative image from the respective data set is presented in A, E, I and the processed image after object detection in B, F and J, respectively. A high magnification of detected cells is presented in C, G, and K. Correlations between the manually quantified images and the quantifications by the algorithm are presented in D, H and L. Iba1-FL-IHC images (M) downloaded from an online repository were inverted (N) and processed. A large proportion of visible cells can be detected by the algorithm (O). All manual quantifications correlated highly with the automated quantification (D, H, L, and P).
Fig 5.
Optimal image settings for object detection of Iba1 immunolabelled cells in DAB.
Original Iba1-DAB immunolabelled brightfield images from one animal (A-BVII) as EDF image of the entire z-stack (A) or as individual focal planes (B-BVII). The corresponding analysed image (C-DVII) and a high magnification of the analysed image (E-FVII) demonstrate that only the EDF image is able to identify the majority of immunolabelled cells in the stack correctly. The same z-stack is imaged under different exposure settings (25ms– 400ms) to utilise different parts of the dynamic range of the camera chip. The images are presented before processing (F-N) and after processing (FI-NI) as well as in high magnification (FII-NII). The histogram above the column displays the range of pixel values taken at the respective exposure setting.