ABSTRACT
Medical image segmentation plays a crucial role in diagnosing and staging diseases. It facilitates image analysis and quantification in multiple applications, but building the right appropriate solutions is essential and highly reliant on the features of different datasets and computational resources. Most existing approaches provide segmentation for a specific anatomical region of interest and are limited to multiple imaging modalities in a clinical setting due to their generalizability with high computational requirements. To mitigate these issues, we propose a robust and lightweight deep learning real-time segmentation network for multi-modality medical images called MISegNet. We incorporate discrete wavelet transform (DWT) of the input to extract salient features in the frequency domain. This mechanism allows the neurons’ receptive field to enlarge within the network. We propose a self-attention-based global context-aware (SGCA) module with varying dilation rates to enlarge the field of view and designate the importance of each scale that enhances the network’s ability to discriminate features. We build a residual shuffle attention (RSA) mechanism to improve the feature representation of the proposed model and formulate a new boundary-aware loss function called Farid End Point Error (FEPE) that correctly segments regions with ambiguous boundaries for edge detection. We confirm the versatility of the proposed model by performing experiments against eleven state-of-the-art segmentation methods on four datasets of different organs, including two publicly available datasets (i.e., ISBI2017, and COVID-19 CT) and two private datasets (i.e., ovary and liver ultrasound images). Experimental results prove that the MISegNet with 1.5M parameters, outperforms the state-of-the-art methods by 1.5%–7% (i.e., dice coefficient score) with a corresponding 23× decrease in the number of parameters and multiply-accumulate operations respectively compared to U-Net.
ABSTRACT
COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%-higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.