  In recent years, "U-shaped" neural networks featuring encoder and decoder
structures have gained popularity in the field of medical image segmentation.
Various variants of this model have been developed. Nevertheless, the
evaluation of these models has received less attention compared to model
development. In response, we propose a comprehensive method for evaluating
medical image segmentation models for multi-indicator and multi-organ (named
MIMO). MIMO allows models to generate independent thresholds which are then
combined with multi-indicator evaluation and confidence estimation to screen
and measure each organ. As a result, MIMO offers detailed information on the
segmentation of each organ in each sample, thereby aiding developers in
analyzing and improving the model. Additionally, MIMO can produce concise
usability and comprehensiveness scores for different models. Models with higher
scores are deemed to be excellent models, which is convenient for clinical
evaluation. Our research tests eight different medical image segmentation
models on two abdominal multi-organ datasets and evaluates them from four
perspectives: correctness, confidence estimation, Usable Region and MIMO.
Furthermore, robustness experiments are tested. Experimental results
demonstrate that MIMO offers novel insights into multi-indicator and
multi-organ medical image evaluation and provides a specific and concise
measure for the usability and comprehensiveness of the model. Code:
https://github.com/SCUT-ML-GUO/MIMO
