Figure 4.

SAND outperformed other pipelines on low numbers of ground truth labels using the CaImAn datasets. A, When trained with low numbers of ground truth neurons, SAND outperformed all other methods on the K53 video. SAND had the highest F₁ and precision of all methods. See Extended Data Figure 2-1C and Table 2-4 for more details. B, When trained with low numbers of ground truth neurons, SAND outperformed all other methods on the J115 video. SAND outperformed CaImAn and Suite2p, but not SUNS on the (C) J123 and (D) YST videos when trained on low numbers of ground truth neurons. Dots represent the average F₁ score for each model when processing the test video(s). Lines represent the mean F₁ scores averaged over bins grouped by the number of training labels; bins spanned 0–50 labels, 50–100 labels, etc. Shaded regions represent standard error. Horizontal lines are the average F₁ scores of Suite2p and CaImAn. The red line (SAND) represents ensemble learning and hyperparameter optimization with FLHO. The blue line represents single-model supervised learning and hyperparameter optimization with FLHO. The orange line (SUNS) represents single-model supervised learning and grid search hyperparameter optimization. See Extended Data Figures 4-1, 4-2, 4-3, 4-4, and 4-5, Table 4-1, and Table 4-2 for more details.