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

arXiv 2306.00446 ↗First posted 1 Jun 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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

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References · 16

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4 more
  1. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  2. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
  3. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  4. 2022Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Modelscited by 3

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