Related papers: Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

URL: http://arxiv.org/abs/2402.07192v1
Date: Sun, 11 Feb 2024 12:58:42 GMT
Title: Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
Authors: H. Fabelo, S. Ortega, D. Ravi, B. R. Kiran, C. Sosa, D. Bulters, G. M. Callico, H. Bulstrode, A. Szolna, J. F. Pineiro, S. Kabwama, D. Madronal, R. Lazcano, A. J. OShanahan, S. Bisshopp, M. Hernandez, A. Baez-Quevedo, G. Z. Yang, B. Stanciulescu, R. Salvador, E. Juarez, R. Sarmiento
Abstract summary: Surgery for brain cancer is a major problem in neurosurgery. The identification of the tumor boundaries during surgery is challenging. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Related papers

Brain Tumor Classification From MRI Images Using Machine Learning [0.24739484546803336]
Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The use of deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumors. The objective of this project is to develop a predictive system for brain tumor detection using machine learning.
arXiv Detail & Related papers (2024-07-15T11:30:40Z)
Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
arXiv Detail & Related papers (2024-05-13T15:40:56Z)
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
arXiv Detail & Related papers (2023-04-11T02:45:38Z)
Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
arXiv Detail & Related papers (2021-08-08T11:26:34Z)
MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification [0.9176056742068814]
This paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The model achieved promising results as compared to existing state-of-the-art models.
arXiv Detail & Related papers (2021-07-26T16:51:00Z)
Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
arXiv Detail & Related papers (2021-07-12T09:04:20Z)
Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model [3.6328238032703806]
We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
arXiv Detail & Related papers (2021-04-06T07:25:52Z)
Brain Tumor Classification Using Medial Residual Encoder Layers [9.038707616951795]
Cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. We propose a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.
arXiv Detail & Related papers (2020-11-01T21:19:38Z)
Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
arXiv Detail & Related papers (2020-07-02T12:00:53Z)
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images [8.75217589103206]
We propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. The proposed method is evaluated on the BraTS 2015 dataset, and the average gained DICE score is 0.73, which is an acceptable result for this application.
arXiv Detail & Related papers (2020-02-26T14:10:40Z)
Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.