Title: MeasureNet: Polyline Detection Based Measurement for Celiac Disease Identification and Grading
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Medical Images, Measurement, Celiac Disease Detection

Abstract: Celiac disease is an autoimmune disorder triggered by the consumption of gluten. It causes damage to the villi, the finger-like projections in the small intestine that are responsible for nutrient absorption. Additionally, the crypts, which form the base of the villi, are also affected, impairing the regenerative process. The deterioration in villi length, computed as the villi-to-crypt length ratio, is one indicator of the severity of the disease. However, manual measurement of villi-to-crypt length can be both time-consuming and susceptible to inter-observer variability.

Automated systems may perform measurement as a post-hoc process over segmentation outputs, but developing a villi/crypt segmentation model requires costly expert annotation, which is made worse by complex villi structures and unclear crypt boundaries. In response, our pathologists provide inexpensive annotation of (approximate) bisectors of villis/crypts for length measurement, and of auxiliary tissues that represent villi and crypt boundaries. To use such annotation, we propose MeasureNet, a polyline detection framework that integrates polyline localization with object-driven losses tailored for measurement tasks. Additionally, we leverage a segmentation model that provides auxiliary guidance on crypt locations when crypts are partially visible. To prevent over-reliance on (noisy) segmentation masks, we employ a mask feature mixup technique. We validate MeasureNet on our novel dataset CeDeM, and also a publicly available DeepBacs dataset. Compared to best SoTA baseline, MeasureNet obtains 9 and 11 percent points improvement in binary and multi-class grading of celiac disease.

TL;DR: We propose MeasureNet, a polyline based detection framework for identification and grading of celiac disease based on length measurement.