Learning Polyline Geometry for Length Measurement in Medical & Biological Images

Object length measurement is a common objective in many biological and medical imaging applications, such as in identifying a tumor's longest axis, tracking larval growth, or measuring duodenal villi height. Conventional deep learning pipelines typically compute length as a byproduct of dense segmentation mask outputs. In this work, we question whether segmentation is even required if length measurement is the primary objective and propose polyline annotations for length measurement as an alternative. A polyline is defined as a sequence of $K$ points along an object's principal axis; we posit that it is inexpensive to annotate yet sufficient for measurement. We introduce MeasureNet, a framework that directly predicts polylines using specialized, measurement aware loss functions. We evaluate MeasureNet across five datasets spanning brightfield and fluorescence microscopy, optical coherence microscopy, computed tomography, and H&E histopathology, including on CeDeM, a newly curated duodenal biopsy dataset with application to Celiac disease detection. Across these measurement datasets, MeasureNet trained with polyline annotations outperforms SoTA segmentation models trained on dense segmentation masks, when controlled for the annotation budget, reducing length measurement Mean Absolute Error (MAE) by up to 30 points. Furthermore, MeasureNet outperforms the closest length measurement baselines across all datasets in measurement as well as localization, yielding up to 12.87 point MAE and 11.56 point mAP gains.