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

Abstract

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 Covid-19 outbreak. In the robotic field, S emantic S egmentation 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 d atasets, w ith e ach dataset being validated through a five-fold c ross-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 o f C ovid-19 C T segmentation. I. INTRODUCTION As of late 2019, the world faces the worst pandemic in years, with the new coronavirus disease, COVID-19, becoming a threat worldwide [1]. According to the global case count from the Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (updated April 20th, 2021), there are a total of 196,743,788 cases identified a ll around the globe, with a total of 4,201,812 global deaths [2]. Early diagnosis is one of the most effective ways to fight against the virus [3], with automatic detection of Covid-19 presence in CT being highly desirable, and recent results showing effectiveness in diagnosing and identifying Covid-19 patients [4]. Semantic Segmentation [5] of CT is one of many research fields of automatic detection of Covid-19 and was widely explored since the Covid-19 outbreak [4]. In the robotic field, S emantic S egmentation o f o rgans a nd C Ts are widely used in robots developed for surgery tasks [6], [7]. Aiming to perform the segmentation of Covid-19 CTs, many studies apply Deep Learning techniques and Deep Neural Networks, achieving impressive results in the task [4]. Deep Neural Networks are widely applied in segmentation problems due to their great generalization capacity, learning to represent different classes of objects [8], [9].However, with new approaches being proposed quickly, an urgency aggravated by the global pandemic, the need

Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation

“…The encoder-decoder network chosen to evaluate the dataset augmentations was the RegNetx-002 [Xu et al 2021] encoder and U-net++ [Zhou et al 2018] decoder. Since the encoders achieved close results in the comparison performed in [Krinski et al 2021], the RegNetx-002 was chosen due to being the network with a smaller number of parameters, making the RegNetx-002 faster for training. The U-net++ was chosen because it achieved the highest F-score compared with other decoders [Krinski et al 2021].…”

“…Since the encoders achieved close results in the comparison performed in [Krinski et al 2021], the RegNetx-002 was chosen due to being the network with a smaller number of parameters, making the RegNetx-002 faster for training. The U-net++ was chosen because it achieved the highest F-score compared with other decoders [Krinski et al 2021]. The evaluation of how data augmentation affects the results of different encoders and decoders was left for future evaluation.…”

“…One of the problems pointed out in [Krinski et al 2021] was the class imbalance due to several images with just the background class; in fact, recent work has shown that several problems suffer from class imbalance [Laroca et al 2021, Laroca et al 2022. Therefore, to mitigate this problem, in the first step of this evaluation, we removed from the datasets images with no lesion in the ground-truth mask.…”

“…Automatic detection of shows to be a great help for early diagnoses [Shi et al 2021], with the Semantic Segmentation [Cao and Bao 2020] of CTs being widely explored since the COVID-19 outbreak [Shi et al 2021]. Deep Learning based techniques and Deep Neural Networks achieved impressive results in the segmentation of COVID-19 CTs [Shi et al 2021, Krinski et al 2021]. However, it has two limiting factors.…”

“…With new approaches being proposed quickly, an urgency aggravated by the global pandemic, the need for a proper evaluation becomes apparent. A broad benchmark of architectures was presented by [Krinski et al 2021], and one of their conclusions was that the models' generalization was impaired by the small number of samples on the field's datasets which also suffer from class imbalance introducing some bias to the models. Data augmentation can mitigate this issue; however, the influence of data augmentation during training was left out.…”