Distinguishing Adenocarcinomas From Granulomas in The CT Scan of The Chest: Performance Degradation Evaluation in The Automatic Segmentation Framework
Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted.
Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.
Figure 1
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Posted 13 Jan, 2021
Received 22 Jan, 2021
On 22 Jan, 2021
On 10 Jan, 2021
On 06 Jan, 2021
Invitations sent on 06 Jan, 2021
On 06 Jan, 2021
On 06 Jan, 2021
On 27 Nov, 2020
Distinguishing Adenocarcinomas From Granulomas in The CT Scan of The Chest: Performance Degradation Evaluation in The Automatic Segmentation Framework
Posted 13 Jan, 2021
Received 22 Jan, 2021
On 22 Jan, 2021
On 10 Jan, 2021
On 06 Jan, 2021
Invitations sent on 06 Jan, 2021
On 06 Jan, 2021
On 06 Jan, 2021
On 27 Nov, 2020
Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted.
Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.
Figure 1
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.