Brain Tumors: How Can Images and Segmentation Techniques Help?

Veloz, Alejandro; Orellana, Antonio; Vielma, Juan; Salas, Rodrigo; Chabert, Stéren

doi:10.5772/22466

Cited by 9 publications

(2 citation statements)

References 75 publications

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“…The 5-year survival of patients with early diagnosis is approximately 54%, while it is only 4% for those who are initially diagnosed at stage 4 cancer. 1 Imaging technology is of paramount significance for the evaluation of lung tumors, 2 since it can facilitate early diagnosis and treatment for such malignancies by essentially discovering tumors at early stages. Positron emission tomography (PET) is an important 3-dimensional imaging technique for lung tumor detection.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging

et al. 2019

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Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of “computer-aided diagnosis” is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.

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Section: Introductionmentioning

confidence: 99%

Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging

et al. 2019

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“…Brain tumor identification can be challenging for tissue segmentation procedures. Apart from their diversity in shape, size, and location, the presence of a tumor in brain images can be associated with a highly heterogeneous and diffuse signal distribution that can be confounded by a similar range of signal intensity in the neighboring intact tissue (Veloz et al, 2011 ). Indeed, our initial analysis using the original implementation of ALI (Seghier et al, 2008 ) was able to identify the correct location of the tumors but was not always able to delineate their full extent.…”

Section: Introductionmentioning

confidence: 99%

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

et al. 2013

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Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.

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