Automatic MRI Brain Tumor Segmentation Techniques: A Survey

Basir, Otman; Shantta, Kalifa

doi:10.21013/jas.v16.n2.p2

Cited by 1 publication

(6 citation statements)

References 51 publications

(57 reference statements)

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“…Fig. 3 reveals classes of Glioma which are provided by AANS under the grades defined by WHO in which Lowgrade gliomas include oligodendrogliomas and astrocytoma's, while high-grade gliomas include glioblastoma multiform [1,2,6,7,12,13]. AANS showed the most broadly used marking scheme developed by WHO (World Health Organization) to classify the histological characteristics of brain tumors (especially for Glioma) under the magnifying lens into categories (Grade 1 to Grade 4) depending on their malignancy or benignity, shows in Fig.…”

Section: Motivation Of the Workmentioning

confidence: 99%

“…Medical imaging is the method of taking images of the human body's internal organs and analyzing them for diagnostic and therapeutic reasons. The study of human tissues and organs is a further application of this approach [12]. Usually, Biopsy is used for diagnosis purposes, but it is intrusive which may cause bleeding and injury [3].…”

Section: Overview Of Medical Image Analysismentioning

confidence: 99%

“…Before any therapy or treatment segmentation is necessary to save healthy tissues because during therapy there is a maximum chance of damaging healthy tissues while damaging and destroying malignant cells [1]. Accurate segmentation of Brain MRI influences all the analysis of results and assisted in surgical planning, visualization, pre & post-operative observations, measurement of brain structures, delimiting lesions, evaluation of disease advancement, quantitative analysis, and improving the survival rate [9,12]. Tasks involved in segmentation are shown in Fig.…”

Section: Brain Neoplasm Segmentation Techniquementioning

confidence: 99%

“…Brain tumors are manually segmented by either manually drawing the area of anatomical structures with distinct labels or manually marking the boundary of the tumor along with structures of interest. Clinical doctors (radiologists, anatomists, and trained technologists) apply extra knowledge, such as brain anatomy, and physiological knowledge on which the accuracy of segmentation depends got through training and experience in addition to the multi-modality information provided in the MR images while doing manual segmentation [1,12,20]. To make drawing regions of interest and image presentation easier during manual demarcation, software solutions with advanced graphical user interfaces are needed.…”

Section: Manual Segmentation Approachmentioning

confidence: 99%

“…In actuality, locating the location of a tumor or the region of interest is a laborious, error-prone, costly, and time-consuming process. Multiple two-dimensional cross-sections (slices) are produced in slices using MRI scanners and a radiologist must examine the multiple slices of the images dataset to choose the most illustrative ones (diagnose tumor) from which the pertinent areas are gently demarcated [12,20]. A single image is often used for manual segmentation of brain tumors, with an injected contrast agent providing intensity augmentation.…”

Section: Manual Segmentation Approachmentioning

confidence: 99%

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Recent Segmentation Trends of MRI-Based Brain Neoplasm: A Review

Siddiqui,

Qadri,

Tayyab

et al. 2023

JAET

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A significant increase in medical-related cases of brain neoplasm has been noticed in the past few years affecting not only adults but children as well. Brain neoplasm segmentation isolated the different abnormal brain tissues from normal brain tissues which are complicated tasks in medical image examination. But simultaneously it plays a crucial role in time diagnosis which not only improved the treatment possibilities but also increased the rate of survival of the patients because a brain neoplasm is a treatable kind of cancer if diagnose well on time. Magnetic resonance imaging (MRI) based segmentation are a more focused, attractive, and attentive study area in recent few years because MRI is noninvasive imaging, safe, and cost-effective. Detection of brain neoplasm using the manual procedure of segmentation is an extremely difficult, time-consuming, expensive, and individual task because large data of MRI images are produced in clinical practice which may delay the diagnosis. This increases the practical significance of the automatic segmentation techniques but due to brain neoplasm being extremely unpredictable concerning position, appearance, type, and size improving the methods for state-of-the-art segmentation process remain a complex task. To segment the brain neoplasm accurately, automatic segmentation is a solution with better performance. The goal of this paper provides an organized literature survey for recent MRI-based automatic brain neoplasm segmentation techniques with the modern participation of several researchers which helps new researchers in exploring future directions. There are several current surveys focused on traditional methods, but this paper focuses on recent trends including machine learning techniques accompanied by transfer learning, deep learning, neural network, and hybridization. Moreover, this survey also presents the findings and limitations of each article which show the effectiveness of the proposed work. Finally, this survey, found that after over two decades of research, the novel methods for segmenting brain neoplasms using computer-aided techniques are becoming more and more refined and becoming closer to being used often in clinical settings but in terms of computing complexity and memory consumption, these approaches lag.

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