scholarly journals Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 829 ◽  
Author(s):  
Madeleine Shaver ◽  
Paul Kohanteb ◽  
Catherine Chiou ◽  
Michelle Bardis ◽  
Chanon Chantaduly ◽  
...  
Keyword(s):  
Deep Learning ◽  
Outcome Prediction ◽  
Cellular Proliferation ◽  
Genetic Characterization ◽  
Tumor Segmentation ◽  
Learning Approaches ◽  
Resonance Imaging ◽  
Cellular Invasion ◽  
Magnetic Resonance Imaging Mri

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

scholarly journals Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning

2021 ◽  
Vol 19 ◽  
pp. 39-44
Author(s):  
Roque Rodríguez Outeiral ◽  
Paula Bos ◽  
Abrahim Al-Mamgani ◽  
Bas Jasperse ◽  
Rita Simões ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Primary Tumor ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Resonance Imaging

scholarly journals A Case Report and Genetic Characterization of a Massive Acinic Cell Carcinoma of the Parotid with Delayed Distant Metastases

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Anthony C. Nichols ◽  
Michelle Chan-Seng-Yue ◽  
John Yoo ◽  
Sumit K. Agrawal ◽  
Maud H. W. Starmans ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Cell Carcinoma ◽  
Large Scale ◽  
Cellular Proliferation ◽  
Genetic Characterization ◽  
Distant Metastases ◽  
Acinic Cell Carcinoma ◽  
Nonsynonymous Mutation ◽  
Acinic Cell

We describe the presentation, management, and clinical outcome of a massive acinic cell carcinoma of the parotid gland. The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. This study provides the first insights into the genetic underpinnings of this cancer. Future large-scale efforts will be necessary to define the mutational landscape of salivary gland malignancies to identify therapeutic targets and biomarkers of treatment failure.

scholarly journals Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging)

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Ida Bagus Leo Mahadya Suta ◽  
Rukmi Sari Hartati ◽  
Yoga Divayana
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Tumor otak menjadi salah satu penyakit yang paling mematikan, salah satu jenis yang paling banyak ditemukan adalah glioma sekitar 6 dari 100.000 pasien adalah penderita glioma. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam menganalisa dan mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki resiko kesalahan yang tinggi, untuk itu dibutuhkan suatu cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Convolutional Neural Network (CNN) menjadi salah satu solusi dalam melakukan klasifikasi otomatis dalam citra MRI. CNN merupakan algoritma deep learning yang memiliki kemampuan untuk belajar sendiri dari kasus kasus sebelumnya. Dan dari penelitian yang telah dilakukan, diperoleh hasil bahwa CNN mampu dalam menyelesaikan klasifikasi tumor otak dengan akurasi yang tinggi. Peningkatan akurasi diperoleh dengan mengembangkan algoritma CNN baik melalui menentukan nilai kernel dan/atau fungsi aktivasi.

Magnetic Resonance Microscopy of Chemically-Induced Liver Foci

1989 ◽  
Vol 17 (4_part_1) ◽  
pp. 613-616 ◽  
Author(s):  
G. Allan Johnson ◽  
Robert R. Maronpot
Keyword(s):  
Magnetic Resonance ◽  
Imaging Technique ◽  
Relaxation Times ◽  
Basic Research ◽  
Resonance Imaging ◽  
T2 Relaxation ◽  
Spin Lattice ◽  
Chemically Induced ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) is a new imaging technique used in clinical diagnosis. This paper describes extension of the technique to basic research applications–specifically detecting and characterizing chemically-induced liver neoplasms and foci of cellular alteration. Two systems have been built that allow spatial microscopic resolution–more than 100,000 x greater than that of earlier efforts. Use of spin-lattice (T1) and spin-spin (T2) relaxation times permits detailed characterization of the tissue.

scholarly journals Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1199
Author(s):  
Michelle Bardis ◽  
Roozbeh Houshyar ◽  
Chanon Chantaduly ◽  
Alexander Ushinsky ◽  
Justin Glavis-Bloom ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Data Availability ◽  
Resonance Imaging ◽  
Learning Network ◽  
Transrectal Biopsy ◽  
Minimum Number ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network

(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets.

scholarly journals Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks

2021 ◽  
Vol 10 (6) ◽  
pp. 23-27
Author(s):  
Tasmiya Tazeen ◽  
◽  
Mrinal Sarvagya ◽  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Early Detection ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Segmentation Accuracy ◽  
Magnetic Resonance Imaging Mri

Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.

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