Segmenting the hippocampus on brain MRI slices is a time-consuming task. Radiologists often zoom in, adjust the contrast, and carefully try to determine where the hippocampus ends and the amygdala begins. The borders are frequently blurry, the shapes complex, and by the time the process is complete, as much as half an hour may have passed. If another colleague repeats the task, the results can look different from the first attempt.
For a structure as important as the hippocampus, even small differences matter. Its importance has been outlined in our previous blog, while this article focuses on the challenge of measuring it accurately.
When radiologists examine the hippocampus on MRI scans, several challenges make segmentation difficult:
The hippocampus is tiny compared to the rest of the brain. Its seahorse-like curves and layered subfields make it anything but simple to outline. Even a few pixels’ difference can significantly alter the volume measurement.
Unlike bones or ventricles, the hippocampus lacks sharp edges. It blends into nearby structures such as the amygdala, parahippocampal gyrus, and temporal horn, forcing radiologists to make judgment calls on each slice.
No two scans are identical. Orientation, patient motion, and contrast settings all affect how the hippocampus appears, making manual standardisation nearly impossible.
Long reporting days take a toll. A minor slip of the mouse or a slightly different interpretation of a boundary is all it takes to introduce variability and across multiple cases, these differences add up.
Even two experienced neuroradiologists may produce slightly different outlines. This inter-rater variability is a recognized limitation and creates real challenges in clinical trials or longitudinal studies, where reliability is critical.
Pathological changes such as atrophy, lesions, surgical alterations, or motion artifacts make hippocampal borders even harder to define. Ironically, these are the very patients where precise measurements are most needed - yet they are also the least reliable to obtain manually.
Instead of spending half an hour tracing the hippocampus manually, radiologists can now upload an MRI Scan file and, within minutes, receive:
No fatigue, no subjective variability, and no waiting. This is what AI-powered segmentation tools are bringing into clinical workflows. Trained on various number of scans, these algorithms can recognize hippocampal borders with a consistency that matches expert tracings, without the limitations of time or exhaustion.
Here’s how AI is reshaping segmentation:
Segmentation is completed in minutes rather than half an hour, removing a major bottleneck in volumetry.
Every scan, every patient, every time — the same rules are applied, eliminating inter-rater variability.
Because the models are trained on diverse datasets, they adapt more effectively to differences in field strength, orientation, and protocols.
Modern platforms don’t produce a black-box result. They provide overlays and quality control checks, allowing radiologists to verify accuracy before signing off.
Whether it’s 20 patients a week or 2,000 across multiple sites, automated tools scale effortlessly, making hippocampal volumetry practical for both research and routine clinical practice.
This also includes the comparisons of a patient’s scans over time.
Instead of spending valuable time drawing boundaries, radiologists can focus on what truly matters:
AI does not replace the radiologist, but it supports them. Handling the repetitive work of segmentation allows experts to apply their skills where they have the greatest impact.
The hippocampus is only the beginning. The same methods can be extended to:
This reflects the future of radiology a shift from purely qualitative reporting toward a combination of visual expertise and quantitative precision.
Manual hippocampal segmentation will always have value in carefully controlled research, but it might not scale in routine clinical practice. The process is slow, variable, and dependent on human judgment
AI is changing that reality. Automating segmentation reduces workload, improves reproducibility, and makes quantitative imaging practical. This allows radiologists to spend less time tracing borders and more time delivering the clinical insights that matter most for patient care.
At Alzevita, we’re building tools to make hippocampal and brain segmentation fast, consistent, and clinically meaningful. Contact us to learn more or request early access.