How can automatic segmentation improve workflow in urology?

In medical imaging, every image tells a story. But before reaching clinical interpretation, a significant portion of clinicians’ time is devoted to technical, repetitive and demanding tasks, such as the manual delineation of anatomical structures.

Automatic medical image segmentation is now emerging as an evolution in clinical practice. Its purpose is to automate the identification and delineation of structures such as the prostate on imaging examinations, particularly MRI. By reducing the cognitive load associated with these preparatory steps, it helps limit repetitive tasks while improving the reproducibility and efficiency of clinical workflows.

The goal is to give clinical expertise back its time. Time for analysis, decision-making, and discussion with teams and patients. In other words, to refocus medical effort on what truly defines the profession: diagnosis and clinical care.

What is automatic segmentation?

In medical imaging, image segmentation consists of identifying and precisely delineating anatomical structures within an examination, so that they can be used for clinical analysis. In the case of the prostate, this step is essential, as it directly impacts volume assessment, lesion analysis, and the planning of diagnostic or therapeutic procedures.

Traditionally, this segmentation is performed manually by the clinician, slice by slice, based on their expertise and interpretation of the images. While this approach remains the reference in many settings, it is time-consuming and relies heavily on the operator’s level of experience.

Automatic segmentation offers a different approach. It relies on algorithms capable of analyzing medical images and autonomously identifying anatomical boundaries, using models trained on expert-annotated data. The clinician’s role then shifts toward review and validation, based on a pre-generated segmentation.

Limitations of current manual segmentation approaches

In daily clinical practice, manual prostate segmentation remains an essential step. However, it involves several constraints that are well recognized by healthcare professionals:

• A significant time investment. Precisely delineating the prostate, slice by slice, requires sustained attention. In already demanding schedules, this task adds to numerous clinical responsibilities, in a context where the number of examinations and patients continues to increase, without a corresponding rise in available clinical time.
• Inter-operator variability. From one clinician to another, and sometimes from one reading to the next, segmentation results may vary. This variability, widely described in medical imaging, can complicate the reproducibility of analyses and the comparison of data over time or across centers.
• Strong dependence on experience. Segmentation quality largely depends on the reader’s level of expertise. The learning curve can be long, particularly for less experienced clinicians or in anatomically complex cases.
• A significant cognitive load. Beyond the technical task itself, segmentation requires continuous concentration, often at the expense of time dedicated to overall image interpretation and clinical decision-making.

Automatic segmentation and workflow in urological imaging

The automation of segmentation is reshaping the organization of work in urological imaging. By intervening upstream of clinical analysis, it transforms a historically manual step into a more structured and consistent process, better integrated into existing workflows.

At the level of a clinical department, this approach supports improved reproducibility of examinations. By limiting variability related to individual practices, it facilitates the comparison of results over time, as well as between different professionals or centers. This level of consistency becomes a key consideration in clinical environments where examination volumes are increasing and collaboration across teams is essential.

By standardizing certain preparatory steps, automation also contributes to a more efficient use of clinical time. It allows teams to focus more on analysis, decision-making and clinical exchange, while maintaining a high level of quality and consistency in the data produced.

Conclusion

As clinical practices evolve and examination volumes continue to rise, the organization of work in urological imaging adapts accordingly. In this context, automatic segmentation approaches, particularly those based on artificial intelligence tools, are positioned as assistance tools designed to support, rather than replace, the clinician.