Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT’s Semantic Segmentation

• Bruno A. Krinski UFPR
• Daniel V. Ruiz UFPR
• Eduardo Todt UFPR

Resumo

With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained a lot of attention, and robust methods of Semantic Segmentation of Computed Tomography (CT) turned highly desirable. Semantic Segmentation of CT is one of many research fields of automatic detection of Covid-19 and was widely explored since the Covid-19 outbreak. In the robotic field, Semantic Segmentation of organs and CTs are widely used in robots developed for surgery tasks. As new methods and new datasets are proposed quickly, it becomes apparent the necessity of providing an extensive evaluation of those methods. To provide a standardized comparison of different architectures across multiple recently proposed datasets, we propose in this paper an extensive benchmark of multiple encoders and decoders with a total of 120 architectures evaluated in five datasets, with each dataset being validated through a five-fold cross-validation strategy, totaling 3.000 experiments. To the best of our knowledge, this is the largest evaluation in number of encoders, decoders, and datasets proposed in the field of Covid-19 CT segmentation.

Palavras-chave: COVID-19, Training, Image segmentation, Computed tomography, Semantics, Surgery, Computer architecture