Fluorescent Neuronal Cell Data Set Release - Continuation III
When it comes to evaluating models for data related to Fluorescent Neuronal Cells (FNC), a variety of metrics are suggested for a thorough and accurate assessment. Beyond the commonly used metrics such as Dice coefficient, Mean Intersection over Union (mIoU), F1 score, precision, recall, Absolute Error, Percentage Error, and R2 coefficient, additional evaluation metrics are essential for capturing characteristics specific to neuronal data.
Tracking-Specific Metrics
For longitudinal tracking of neuronal cells, metrics that reward consistent tracking across sessions or days are important. These include cell tracking accuracy over time and track continuity or identity preservation rates, which assess if the same neuron is correctly matched across multiple imaging sessions, as highlighted in longitudinal fluorescence imaging studies [1].
Topological Metrics
Given the complex morphology of neurons (dendrites and axons), metrics that assess the preservation of structural and connectivity features are relevant. Topological similarity or fidelity metrics that evaluate the reconstructed neurite or axon tree structures against ground truth skeletons or annotations [2] and metrics derived from graph theory quantifying connectivity and branching patterns in segmented neuronal structures are useful.
Morphological Metrics
Shape similarity metrics such as Hausdorff distance, average boundary distance, or contour-based measures can evaluate the fidelity of cell shape segmentation beyond pixel overlap. Skeleton-based metrics, comparing the extracted skeleton (thin centerline) of neurites/axons to expert annotations, are also valuable.
Voxel-wise Metrics
Additional voxel-centric evaluation metrics, useful especially in 3D volumes, can include Volumetric Overlap Error or False Discovery Rate (FDR) and False Negative Rate (FNR) at the voxel level, as well as sensitivity and specificity separately at the voxel level to understand false positive/negative segmentation rates.
Statistical and Distributional Metrics
Correlation coefficients on fluorescence intensity distributions where relevant and Structural Similarity Indexes (SSIM) for image or volume similarity evaluation are useful in this context.
Biological/Functional Metrics
When neuronal activity or function is linked to segmentation, metrics that measure the accuracy of signal extraction or event detection (e.g., calcium transient detection accuracy) may be incorporated [5].
These additional metrics aim to capture characteristics specific to neuronal data, such as temporal tracking consistency, topological correctness, and morphological accuracy, which general segmentation metrics alone might miss [1][2][3].
The Absolute Error and Percentage Error
The Absolute Error provides an idea of the actual distance between target and predicted counts, while the Percentage Error gives information on whether the model is over- or under-estimating the counts. However, it is important to note that the segmentation of borders may still have minor repeated errors even when the bulk of cells is correctly recognized, making sole indicator values insufficient for a truthful assessment. The suggestion is to look at the three indicators jointly for a more comprehensive understanding of the model's strengths and weaknesses.
The Choice of Metrics
The final choice of metrics depends on the specific requirements for the analysis. In the case of FNC, each predicted object is compared to all cells in the corresponding ground-truth label and uniquely linked with the closest one, with the predicted element considered a match if their centroids are less distant than the average cell diameter (50 pixels), thereby increasing the true positive count (TP).
Noise in FNC Segmentation
A primary source of noise in FNC segmentation comes from the annotation procedure, where ground-truth labels were produced with a semi-automatic approach involving adaptive thresholding and manual annotation.
Conclusion
The evaluation and performance assessment are critical steps in data analysis pipelines, with each strategy emphasizing different capabilities of the model. By considering a range of complementary metrics, researchers can gain a more nuanced understanding of their models' performance and make more informed decisions about their analyses.
[1] Longitudinal cell tracking metrics rewarding consistent identity preservation across recordings. [2] Topological fidelity measures in neuronal structure segmentation and reconstruction. [3] Morphological and skeleton-based similarity metrics for neuron shape accuracy. [4] Fluorescence intensity correlation and functional signal extraction metrics in multimodal datasets. [5] The Absolute Error provides an idea of the actual distance between target and predicted counts. [6] The segmentation of borders may still have minor repeated errors even when the bulk of cells is correctly recognized, making sole indicator values insufficient for a truthful assessment. [7] The suggestion is to look at the three indicators jointly for a more comprehensive understanding of the model's strengths and weaknesses. [8] In the case of FNC, each predicted object is compared to all cells in the corresponding ground-truth label and uniquely linked with the closest one, with the predicted element considered a match if their centroids are less distant than the average cell diameter (50 pixels), thereby increasing the true positive count (TP). [9] The final choice of metrics depends on the specific requirements for the analysis. [10] A primary source of noise in FNC segmentation comes from the annotation procedure, where ground-truth labels were produced with a semi-automatic approach involving adaptive thresholding and manual annotation. [11] The third article in the series examines a list of metrics suitable for analyzing Fluorescent Neuronal Cells (FNC) data.
Science and medical-conditions data, specifically Fluorescent Neuronal Cells (FNC), require specialized evaluation metrics due to the complex morphology of neurons. Beyond commonly used metrics, it's crucial to consider tracking-specific metrics like cell tracking accuracy over time and track continuity, topological metrics that assess structural and connectivity features, morphological metrics such as Hausdorff distance, and voxel-wise metrics like Volumetric Overlap Error for a thorough and accurate assessment. Technology, specifically data-and-cloud-computing, plays a significant role in the development and application of such metrics for the comprehensive analysis of FNC data.
