Objective: To evaluate surgery results, we established a novel method to digitize nasal morphology with the use of Hausdorff distance and analyzed nose morphology after cheiloplasty.
Study Design: We evaluated the naris after primary cheiloplasty of 30 unilateral cleft lip and palate patients. Similarity between left and right sides was assessed by visual evaluation, area ratio, perimeter ratio, aspect a/u ratio, and Hausdorff distance. The postoperative naris morphology was also compared between 15 patients treated with a Hotz plate before surgery and 15 not treated.
Results: Significant correlation with visual evaluation was found for Hausdorff distance. For the groups with and without Hotz plate treatment, the visual evaluation was higher and Hausdorff distance significantly lower in the treated group.
Conclusions: The morphologic measurement obtained using the Hausdorff distance was the closest to visual evaluation, and assessment using Hausdorff distance suggested that using a Hotz plate helps retain the symmetry of the nares after cheiloplasty.
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http://dx.doi.org/10.1016/j.oooo.2012.01.042 | DOI Listing |
Phys Eng Sci Med
January 2025
School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Periodontology, Semmelweis University, Budapest, Hungary.
Objectives: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.
Materials And Methods: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR.
Phys Imaging Radiat Oncol
October 2024
Université Paris-Saclay, Gustave Roussy, Inserm, Molecular Radiotherapy and Therapeutic Innovation, U1030, 94800 Villejuif, France.
Background And Purpose: Deep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
January 2025
Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Background And Purpose: A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.
Materials And Methods: Twenty prostate cancer patients were enrolled in this prospective clinical study.
Med Biol Eng Comput
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.
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