Semantic Segmentation of Spectral Images: a Comparative Study using FCN8s and U-NET on RIT-18 Dataset

S, Darshik A; Dev, Aditya; Bharath, M; Nair, Bharath Ashok; Gopakumar, G.

doi:10.1109/icccnt49239.2020.9225461

Cited by 4 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the superiority of the proposed multimodal information fusion segmentation network overall framework, this paper compares it with some classic brain tumor segmentation methods on the BraTS18,19 dataset. These methods include some classic segmentation methods FCN8 s, U-net, U-net++, Deep ResUnet (DRU) [45][46][47][48], and the experimental index comparison results are shown in Table I. The article uses several common indicators in image segmentation, Dice Score, Positive Predictive Value (PPV), and Hausdorff Score (HD).…”

Section: B Experimental Detailsmentioning

confidence: 99%

Brain Tumor Segmentation Based on The Learning Statistical Texture

Guo,

Feiba Chang,

Chen

et al. 2023

JAIT

View full text Add to dashboard Cite

Achieving accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) is important for clinical diagnosis and accurate treatment, and the efficient extraction and analysis of MRI multimodal feature information is the key to achieving accurate segmentation. In this paper, we propose a multimodal information fusion method for brain tumor segmentation, aimed at achieving full utilization of multimodal information for accurate segmentation in MRI. In our method, the semantic information processing module (SIPM) and Multimodal Feature Reasoning Module (MFRM) are included: (1) SIPM is introduced to achieve free multiscale feature enhancement and extraction; (2) MFRM is constructed to process both the backbone network feature information layer and semantic feature information layer. Using extensive experiments, the proposed method is validated. The experimental results based on BraTS2018 and BraTS2019 datasets show that the method has unique advantages over existing brain tumor segmentation methods.

show abstract

Section: B Experimental Detailsmentioning

confidence: 99%

Brain Tumor Segmentation Based on The Learning Statistical Texture

Guo,

Feiba Chang,

Chen

et al. 2023

JAIT

View full text Add to dashboard Cite

show abstract

Liver Segmentation with MT-UNet++

Yang,

Sun,

Liang

et al. 2024

IFMBE Proceedings

View full text Add to dashboard Cite

Deep Semantic Segmentation of Trees Using Multispectral Images

Ülkü

Akagündüz

Ghamisi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Forests can be efficiently monitored by automatic semantic segmentation of trees using satellite and/or aerial images. Still, several challenges can make the problem difficult, including the varying spectral signature of different trees, lack of sufficient labelled data, and geometrical occlusions. In this paper, we address the tree segmentation problem using multispectral imagery. While we carry out large-scale experiments on several deep learning architectures using various spectral input combinations, we also attempt to explore whether hand-crafted spectral vegetation indices can improve the performance of deep learning models in the segmentation of trees. Our experiments include benchmarking a variety of multispectral remote sensing image sets, deep semantic segmentation architectures, and various spectral bands as inputs, including a number of hand-crafted spectral vegetation indices. From our large-scale experiments, we draw several useful conclusions. One particularly important conclusion is that, with no additional computation burden, combining different categories of multispectral vegetation indices, such as NVDI, ARVI, and SAVI, within a single three-channel input, and using the state-of-the-art semantic segmentation architectures, tree segmentation accuracy can be improved under certain conditions, compared to using high-resolution visible and/or nearinfrared input.

show abstract

Semantic Segmentation of Spectral Images: a Comparative Study using FCN8s and U-NET on RIT-18 Dataset

Cited by 4 publications

References 13 publications

Brain Tumor Segmentation Based on The Learning Statistical Texture

Brain Tumor Segmentation Based on The Learning Statistical Texture

Liver Segmentation with MT-UNet++

Deep Semantic Segmentation of Trees Using Multispectral Images

Contact Info

Product

Resources

About