Automatic Glomerulus Detection in Renal Histological Images

Glomeruli are microscopic structures of the kidney affected in many renal diseases. The diagnosis of these diseases depends on the study by a pathologist of each glomerulus sampled by renal biopsy. To help pathologists with the image analysis, we propose a glomerulus detection method on renal histological images. For that, we evaluated two state-of-the-art deep-learning techniques: single shot multibox detector with Inception V2 (SI2) and faster region-based convolutional neural network with Inception V2 (FRI2). As a result, we reached: 0.88 of mAP and 0.94 of F1-score, when using SI2, and 0.87 of mAP and 0.97 of F1-score, when using FRI2. On average, to process each image, FRI2 required 30.91s, while SI2 just 0.79s. In our experiments, we found that SI2 model is the best detection method for our task since it is 64% faster in the training stage and 98% faster to detect the glomeruli in each image.