Subcutaneous chest wall hamartoma: Case report

Hawariat, Binyam Yohannes Wolde; Fenta, Bizunesh Dires; Gebreselassie, Hana Abebe

doi:10.1016/j.epsc.2021.102009

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The X-ray equipment commonly used in hospitals works on a principle in which particles of X-rays are irradiated to the body and transmitted, and the remaining X-rays are detected in the film [29]. Because the radiation density in our body is higher in the order of air, fat, organs, and bones, the lung area containing the most air appears dark, and the bone with the highest density appears bright in the X-ray image [30].…”

Section: Discussionmentioning

confidence: 99%

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning

Lee

Kim

2022

Sensors

View full text Add to dashboard Cite

Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been proposed to focus on the desired target. However, it is challenging to segment ribs elaborately using deep learning because they cannot reflect the characteristics of each region. Identifying which region has specific characteristics vulnerable to deep learning is an essential indicator of developing segmentation methods in medical imaging. Therefore, it is necessary to compare the deep learning performance differences based on regional characteristics. This study compares the differences in deep learning performance based on the rib region to verify whether deep learning reflects the characteristics of each part and to demonstrate why this regional performance difference has occurred. We utilized 195 normal chest X-ray datasets with data augmentation for learning and 5-fold cross-validation. To compare segmentation performance, the rib image was divided vertically and horizontally based on the spine, clavicle, heart, and lower organs, which are characteristic indicators of the baseline chest X-ray. Resultingly, we found that the deep learning model showed a 6–7% difference in the segmentation performance depending on the regional characteristics of the rib. We verified that the performance differences in each region cannot be ignored. This study will enable a more precise segmentation of the ribs and the development of practical deep learning algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning

Lee

Kim

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The term “hamartoma” is derived from the Greek words "hamartia" (defect, error) and "oma" (tumor-like growth) [ 1 ]. Hamartoma is defined as a non-neoplastic, unifocal/multifocal, developmental abnormality, encompassing a blend of cytologically normal mature tissues and cells that are native to the anatomic site, and exhibits a disordered architectural pattern with a preponderance of one of its components.…”

Section: Introductionmentioning

confidence: 99%

Infantile Hemangioma of the Upper Lip: Report of a Rare Case With a Brief Review of Literature

Hasan¹,

Khan²,

Banerjee³

et al. 2023

Cureus

View full text Add to dashboard Cite

Hamartomas are tumor-like abnormalities typified by the presence of cellular proliferation indigenous to the native site. However, hamartomas maintain growth cessation without the potential for further growth or malignant transformation. Hamartomas are commonly seen in the lungs, kidney, liver, and spleen and rarely occur in the orofacial region. Various hamartomatous oral lesions include hemangiomas, lymphangiomas, tori, exostosis, dens invaginatus, dens evaginatus, odontomas, nevi, and cherubism. Infantile hemangiomas are benign vascular tumors that emerge soon after birth and experience rapid growth within the first year. Oral hemangiomas affect up to 6.4% of infants and are more common on the ventral surface of the tongue, as opposed to oral vascular malformations, which are more prevalent on the lips. It also has a 3:1 female-to-male predominance. Afterward, their growth usually stabilizes and enters a prolonged, incomplete involution phase. Uncomplicated hemangiomas generally exhibit spontaneous resolution, whereas few can leave behind scars and telangiectasias on the external surface of the skin on which it occurs. Thus, lesions located in anatomically sensitive regions necessitate vigilant surveillance and treatment. This paper deals with an asymptomatic swelling of the upper lip in a four-year-old female child but with problems in aesthetics, speech, and feeding. A thorough history, clinical examination, positive diascopy, ultrasonography, and histopathology confirmed the diagnosis of infantile hemangioma.

show abstract

Subcutaneous chest wall hamartoma: Case report

Cited by 2 publications

References 16 publications

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning

Infantile Hemangioma of the Upper Lip: Report of a Rare Case With a Brief Review of Literature

Contact Info

Product

Resources

About