Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

2010 ◽  
Author(s):  
Cherry Ballangan ◽  
Xiuying Wang ◽  
Stefan Eberl ◽  
Michael Fulham ◽  
Dagan Feng
Keyword(s):  
Lung Tumor ◽  
Region Growing ◽  
Whole Body ◽  
Tumor Segmentation

scholarly journals Lung tumor segmentation using improved region growing algorithm

2020 ◽  
Vol 52 (10) ◽  
pp. 2313-2319 ◽  
Author(s):  
Jamshid Soltani-Nabipour ◽  
Abdollah Khorshidi ◽  
Behrooz Noorian
Keyword(s):  
Lung Tumor ◽  
Region Growing ◽  
Tumor Segmentation

AMRSegNet: adaptive modality recalibration network for lung tumor segmentation on multi-modal MR images

2021 ◽  
Author(s):  
Jiaxin Li ◽  
Houjin Chen ◽  
Yanfeng Li ◽  
Yahui Peng ◽  
Naxin Cai ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Segmentation ◽  
Mr Images

scholarly journals Primary pulmonary epithelioid sarcoma: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Eiki Mizutani ◽  
Riichiro Morita ◽  
Keiko Abe ◽  
Makoto Kodama ◽  
Shogo Kasai ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Lung Tumor ◽  
Smooth Muscle Actin ◽  
Asbestos Exposure ◽  
Epithelioid Sarcoma ◽  
Whole Body ◽  
Health Examination ◽  
Positron Emission ◽  
Primary Lung Tumor ◽  
The Right

Abstract Background Epithelioid sarcoma most frequently occurs in the dermal or subcutaneous area of the distal extremities. To date, there have been three cases of primary pulmonary epithelioid sarcoma reported. We report a case of epithelioid sarcoma that is considered a primary lung tumor. Case presentation A 65-year-old asymptomatic Asian male patient underwent chest radiography during a routine health examination, and an abnormal mass was detected. His past medical history was unremarkable. He smoked 40 cigarettes every day and had slightly obstructive impairment on spirometry. He worked as an employee of a company and had no history of asbestos exposure. He underwent partial resection of the right lung by thoracoscopy. A histological examination of the tumor revealed a cellular nodule of epithelioid and spindle-shaped cells. Some of the tumor cells displayed rhabdoid features and reticular arrangement in a myxomatous stroma. Immunohistochemically, the tumor cells were positive for vimentin, smooth muscle actin (SMA), CD34, and epithelial membrane antigen (EMA); loss of the BAF47/INI1 protein in the tumor cells was also confirmed. A diagnosis of epithelioid sarcoma was established. Careful screening by whole-body positron emission tomography for another primary lesion after surgery did not detect any possible lesion. He had no cutaneous disease. Conclusion To our knowledge, this is the fourth case of a proximal-type epithelioid sarcoma considered as a primary lung tumor.

scholarly journals Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

2018 ◽  
Vol 7 (2.6) ◽  
pp. 306
Author(s):  
Aravinda H.L ◽  
M.V Sudhamani
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Ct Images ◽  
Higher Order ◽  
Liver Carcinoma ◽  
Tumor Segmentation ◽  
Abdominal Ct ◽  
Simple Linear Iterative Clustering ◽  
Classification Of Tumors ◽  
Legendre Moments

The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

scholarly journals Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4585
Author(s):  
Wouter R. P. H. van de Worp ◽  
Brent van der Heyden ◽  
Georgios Lappas ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Tumor Growth ◽  
Tumor Volume ◽  
Learning Algorithm ◽  
Lung Tumor ◽  
Whole Body ◽  
Ct Scans ◽  
Therapeutic Effects ◽  
Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

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