Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation
This provides a comprehensive comparison for researchers and practitioners in medical imaging and robotics to select effective methods for COVID-19 diagnosis and surgical tasks, though it is incremental as it focuses on benchmarking rather than introducing new methods.
The paper tackles the need for standardized evaluation of semantic segmentation methods for COVID-19 CT scans by conducting an extensive benchmark of 120 encoder-decoder architectures across five datasets, totaling 3,000 experiments, which is the largest such evaluation in the field.
With the COVID-19 global pandemic, computerassisted 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 Covid19 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.