CT Scan Based Brain Tumor Recognition and Extraction using Prewitt and Morphological Dilation

Author(s):  
Akanksha Soni ◽  
Avinash Rai
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.


2021 ◽  
pp. 290-297
Author(s):  
Sanjay Kumar ◽  
J.N. Singh ◽  
Naresh Kumar

The cerebrum tumors are the most well-known and forceful sickness, prompting an extremely short future in their most noteworthy evaluation. Accordingly, treatment arranging is a key stage to improve the personal satisfaction of patients. Generally, various medical image modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and ultrasound image are used to evaluate the cerebrum tumor in a brain, lung, liver, breast, prostate etc. MRI images are very much useful for different types of brain tumor exposure and segmentation. A plethora of methods like k-means clustering, Fuzzy C-Means, SOM clustering, Deep Convolution Neural Networks (DNN), SVM, Convolutional Neural Networks (CNN) for cerebrum brain tumor detection from MRI images. This paper concentrated on mind cerebrum tumor recognition calculations that have been planned so distant to recognize the area of the cerebrum tumor.


2021 ◽  
Vol 2026 (1) ◽  
pp. 012023
Author(s):  
Hailan Yu ◽  
Gengrun Yao ◽  
Hang Xu ◽  
Ruoshan Xiong

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. TPS2662-TPS2662
Author(s):  
Behnam Badie ◽  
Michael E Barish ◽  
Ammar Chaudhry ◽  
Massimo D'Apuzzo ◽  
Stephen J. Forman ◽  
...  

TPS2662 Background: Glioblastoma (GBM) is the most common and most aggressive primary brain tumor. Around 294,900 new cases are diagnosed globally with 241,000 deaths each year. The 5-year survival is only 5%. Median overall survival from first recurrence is only 5-8 months. There is no established standard of care for recurrent GBM. City of Hope (COH) has developed and optimized a CAR T cell therapy utilizing the chlorotoxin peptide (CLTX) as the CAR’s tumor recognition domain against GBM. CLTX-CAR T cells specifically and broadly target GBM through recognition of a receptor complex including membrane-bound matrix metalloprotease 2 (MMP-2). CLTX-CAR T cells do not exhibit off-tumor recognition of normal human or murine cells and tissues in preclinical models. In in vitro studies, COH evaluated patient-derived brain tumor (PBT) cell lines for CLTX binding and expression of IL13Rα2, HER2 and EGFR, three targets of CAR T cell trials for GBMs. Strong CLTX binding to tumor cells was observed in of the majority of primary GBM lines, independent of these other antigens. In preclinical studies using in vivo mouse models, a single intratumoral (ICT) injection of CLTX-CAR T cells (1×106 CAR+ T cells) exhibited robust anti-tumor activity against ffLuc+ PBT106 tumors orthotopically-engrafted in NSG mice. Overall, when compared to mice treated with mock-transduced Tn/mem (no CAR) T cells, the CLTX(EQ)28ζ/CD19t+ T cells reduced tumor burden and significantly increased survival. Taken together, these preclinical findings support the potential safety and efficacy of CLTX-CAR T cells, and provide the rationale for clinical testing of this therapy. As cellular heterogeneity intrinsic to GBM likely contributes to resistance to therapy and limited response rates, CLTX-CAR T cells may provide greater tumor eradication in a higher proportion of patients with GBM. Methods: This study is a phase 1, single center, safety and maximum tolerated dose (MTD) finding study of CLTX-CAR T cells for subjects with MMP2+ recurrent or progressive GBM. A safety lead-in of 3−6 participants receiving CLTX-CAR T cells by ICT delivery will be completed first. Subsequently, subjects would receive cells administered through both ICT and intraventricular (ICV catheters) (i.e. dual delivery) in two dose schedules. Subjects will be evaluated for safety and tolerability, and may continue to receive treatment until disease progression. Time to progression, overall survival, and disease response by Response Assessment in Neuro-Oncology (RANO) criteria, will be evaluated and descriptively compared to historical data. The study is actively enrolling patients. Clinical trial information: NCT04214392.


2019 ◽  
Vol 10 ◽  
pp. 256
Author(s):  
Erika Yamazawa ◽  
Yoshitaka Honma ◽  
Kaishi Satomi ◽  
Hirokazu Taniguchi ◽  
Masamichi Takahashi ◽  
...  

Background: Small bowel adenocarcinoma (SBA) accounts for <2% of all gastrointestinal malignancies. The most common organs of SBA metastases are the abdominal lymph node, liver, and peritoneum. There have been almost no reports of brain metastases of SBA. Dabaja et al. reported 1 case of brain metastasis out of 217 SBA cases, but details of the clinical course of the case were unclear. Our case might be the first report covering the full clinical course, pathological findings, and genetic data. Here, we report a very rare case of brain metastasis from poorly differentiated SBA. Case Description: A 54-year-old man who suffered from abdominal pain and melena visited a nearby hospital. This patient had no risk factors for SBA. He underwent partial resection of the jejunum with regional lymphadenectomy and combined resection of the transverse colon. Pathological diagnosis was poorly differentiated adenocarcinoma, pT4N2M0 Stage IIIB (UICC-TNM: 8th edition). One month after curative surgery, liver metastasis was detected by a computed tomography (CT) scan, and then, palliative chemotherapy was started. During the third-line chemotherapy, a brain tumor on the left cerebellum was detected by the CT scan. Tumor resection was performed, and the histopathological features coincided with the primary jejunum tumor. Based on surgical, radiological, pathological, and genetic findings, this brain tumor was comprehensively diagnosed as a metastasis from poorly differentiated SBA. Conclusion: Here, we experienced a very rare case of brain metastasis from poorly differentiated SBA.


Sign in / Sign up

Export Citation Format

Share Document