LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation. (May 2021)

• Record Type: Journal Article
• Authors: Dutande, Prasad; Baid, Ujjwal; Talbar, Sanjay
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Nodule -- Maximum Intensity Projection -- Squeeze -- CNN; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102527
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A convolution operation in CNNs enable networks to derive significant features using spatial and channel-wise information. A wide range of prior research has canvassed the spatial element to strengthen the representational power of CNN. In this paper, instead of spatial element we centered our research on channel-wise element. The UNet (38) is state-of-the-art medical image segmentation network which has encoder-decoder structure with number of covolutional layers. For segmentation of tiny structures like nodules, UNet lacks in representation of significant features. The UNet architecture has long skip connections between encoder and decoder at various levels in order to retain the spatial information. To strengthen the representation power of UNet, we induced the short skip connections called residual connections in the encoder, thereby forming a residual variant of Unet. However, these modifications were not sufficient for effective nodule segmentation. Therefore, we introduce a further modified and improved version of UNet which is termed as SquExUNet. It is a novel nodule segmentation model that utilizes the effectiveness of squeeze and excitation blocks to extract fine-grained information to segment the nodules. The segmentation framework proposed in this paper utilizes SquExUNet and segments the nodule effectively, but the segmentation framework does emphasize on detect-ability of nodules. Consequently, there arises a need of classification network that can classify the nodule candidates yielded in segmentation framework. The nodules and non nodule structures are difficult to distinguish in 2D image due to their similar resemblance. Taking that into consideration, we presented an unique approach for detection of nodules using 2D-3D cascaded CNNs. In this detection framework, primarily segmentation framework is used for collection of nodule candidates; and thereafter, a novel 3D classification model termed as 3D-NodNet classifies nodules and non-nodule volumetric cubes. A novel substantial amount of Indian Lung CT Image Database (ILCID) clinical dataset is included in the study along with publicly available datasets. Abstract: The early detection of lung cancer is attained with the detection of initial stage nodules ( 3 − 30 mm ) which can exorbitantly increase the 5 -year survival rate of lung cancer patients. Nodules are very small size circumscribed structures in the lungs and are difficult to detect due to their size. The identification of nodule is also more challenging due to similarly resembling structures like non-nodules that contains features which could make it identifiable as a nodule. Therefore, to deal with these challenging issues, we proposed a novel approach for segmentation, classification and detection of lung nodules from CT scan images. In our proposed method we involve a maximum intensity projection technique as a part of image preprocessing method. We demonstrated our experimentation and proposed SquExUNet segmentation model and 3D-NodNet classification model on publicly available Lung Image Database Consortium – Image Database Resource Initiative (LIDC), LNDb Challenge Dataset and purely independent Indian Lung CT Image Database (ILCID) clinical dataset. We proposed a 2D-3D cascaded CNN strategy for detection of nodule that yields the accurately segmented and classified nodule. Results obtained with proposed method indicates that we have successfully detected and segmented the lung nodules effectively, compared to existing lung nodule detection and segmentation algorithms. We achieved a Dice-Coefficient metrics of 0.80 for segmentation of nodule and 90.01 % Sensitivity for nodule detection.