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In recent years, "U-shaped" neural networks featuring encoder and decoderstructures have gained popularity in the field of medical image segmentation.Various variants of this model have been developed. Nevertheless, theevaluation of these models has received less attention compared to modeldevelopment. In response, we propose a comprehensive method for evaluatingmedical image segmentation models for multi-indicator and multi-organ (namedMIMO). MIMO allows models to generate independent thresholds which are thencombined with multi-indicator evaluation and confidence estimation to screenand measure each organ. As a result, MIMO offers detailed information on thesegmentation of each organ in each sample, thereby aiding developers inanalyzing and improving the model. Additionally, MIMO can produce conciseusability and comprehensiveness scores for different models. Models with higherscores are deemed to be excellent models, which is convenient for clinicalevaluation. Our research tests eight different medical image segmentationmodels on two abdominal multi-organ datasets and evaluates them from fourperspectives: correctness, confidence estimation, Usable Region and MIMO.Furthermore, robustness experiments are tested. Experimental resultsdemonstrate that MIMO offers novel insights into multi-indicator andmulti-organ medical image evaluation and provides a specific and concisemeasure for the usability and comprehensiveness of the model. Code:https://github.com/SCUT-ML-GUO/MIMO

Evaluation of Multi-indicator And Multi-organ Medical Image Segmentation Models