Fibrous Tissue Semantic Segmentation in CT Images of Diffuse Interstitial Lung Disease.
Interstitial-lung-disease progression assessment and diagnosis via radiological findings on computed tomography images require significant time and effort from expert physicians. Accurate results from these analyses are critical for treatment decisions. Automatic semantic segmentation of radiologica...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 6; pp. 3470 - 3484 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190236350&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190236350 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Dec2025 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190236350 189894345 190236350 190236350 10.1007/s10278-025-01420-x 190236350 ppf: 3470 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fibrous Tissue Semantic Segmentation in CT Images of Diffuse Interstitial Lung Disease. aug: au: Hernández-Vázquez, Natanael Santos-Arce, Stewart R. Hernández-Gordillo, Daniel Salido-Ruiz, Ricardo A. Torres-Ramos, Sulema Román-Godínez, Israel affil: https://ror.org/043xj7k26 División de Tecnologías para la Integración Ciber-Humana, CUCEI-Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Olímpica, 44430, Guadalajara, Jalisco, Mexico sug: subj: Lung Diseases, Interstitial Diagnosis Tomography, X-Ray Computed Utilization Image Processing, Computer Assisted Utilization Fibrosis Diagnosis Funding Source Research Methodology Academic Medical Centers Switzerland Descriptive Statistics Comparative Studies T-Tests Mann-Whitney U Test ab: Interstitial-lung-disease progression assessment and diagnosis via radiological findings on computed tomography images require significant time and effort from expert physicians. Accurate results from these analyses are critical for treatment decisions. Automatic semantic segmentation of radiological findings has been developed recently using convolutional neural networks (CNN). However, on the one hand, few works present individual performance scores for radiological findings that allow for accurately measuring fibrosis segmentation performances; on the other hand, the poorly annotated quality of available databases may mislead researcher observations. This study presents a CNN methodology employing three different architectures (U-net, LinkNet, and FPN) with transfer learning and data augmentation to enhance the performance in semantic segmentation of fibrosis-related radiological findings (FRF). In addition, considering the poor quality of manual CT tagging on available datasets, we use two alternative evaluation strategies, first using only the fibrosis region of interest. Second, re-tagging and validating the test set by an expert pulmonologist. Using DICOM images from the Interstitial Lung Diseases Database, the implemented approach achieves a Jaccard Score Index of 0.7355 with a standard deviation of 0.0699 and a Dice Similarity Coefficient of 0.8459 with a standard deviation of 0.0470 comparable to state-of-the-art performance in FRF semantic segmentation. Also, a visual evaluation of the images automatically tagged by our proposal was performed by a pulmonologist. Our proposed method successfully identifies these FRF areas, demonstrating its effectiveness. Also, the pulmonologist revealed discrepancies in the dataset tags, indicating deficiencies in FRF annotations. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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