Automated luteal blood perfusion assessment in cows using image processing and deep learning algorithms 

Theriogenology. 2026 Aug 25;266:118156. doi: 10.1016/j.theriogenology.2026.118156. Online ahead of print.

ABSTRACT

The aim of this study was to compare luteal blood perfusion (BP) estimated by subjective human evaluation, ImageJ pixel analysis, and a deep learning (DL)-based system. Color Doppler ultrasound (CDU) examinations utilized herein were from three independent studies where CDU videos were collected at embryo transfer (Day 7; early luteal phase; ELP; n = 129), or on Days 15 (mid luteal phase; MLP; n = 48) and 20 (late luteal phase; LLP; n = 47) of pregnancy. Each corpus luteum (CL) was classified as cavitated (CV; cavity ≥10% of luteal area) or non-cavitated (NC; <10%). Luteal BP was estimated using three methods: Human evaluation, ImageJ pixel counting, and a DL pipeline for frame selection, CL segmentation, and BP quantification. Positive linear relationships were observed among BP estimation methods in all datasets (r ≥ 0.52; P < 0.001) and within CL cavity classifications (r ≥ 0.62; P < 0.001). Strong relationships were consistently observed in the LLP dataset (r ≥ 0.79; P < 0.001). No relationship was detected between circulating progesterone (P4) and BP at ELP or MLP (P ≥ 0.14), regardless of the estimation method. Positive linear relationships (P ≤ 0.001) with P4 were observed for Human, ImageJ, and DL estimates at LLP. In conclusion, DL-derived BP estimates showed strong linear relationships, satisfactory agreement, and biological validity comparable to Human and ImageJ methods, supporting its use for automated assessment of luteal BP in cattle.

PMID:42664605 | DOI:10.1016/j.theriogenology.2026.118156