Accuracy and precision of manual segmentation of the maxillary sinus in MR images—a method study

Background The paranasal sinuses are complex anatomical structures, characterised by highly variable shape, morphology and size. With the introduction of multidetector scanners and the development of many post-processing possibilities, computed tomography became the gold standard technique to image the paranasal sinuses. Segmentation allows the extraction of metrical and shape data of these anatomical components that can be applied for diagnostic, education, surgical planning and simulation, and to plan minimally invasive interventions in otorhinolaryngology and neurosurgery. Discussion Our aim was to provide a review of the existing literature on segmentation, its types and application, and the data obtained from this procedure. The literature search was conducted on PubMed (including Medline), ScienceDirect and Google Scholar databases, using the keywords as follows: ‘paranasal sinuses’, ‘frontal sinus’, ‘maxillary sinus’, ‘sphenoid sinus’, ‘ethmoid sinus’, in all possible combinations with the keywords ‘segmentation’ and ‘volumetric analysis’. Inclusion criteria were: articles written in English, on living human subjects, on the adult population and focused on paranasal sinuses analysis. Conclusion This article provides an overview of the types and main application of segmentation procedures on paranasal sinuses, and the results provided by the studies on this topic.

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“…observed the higher accuracy of MR dataset segmentation and the high repeatability of the procedure in both MR and CT analysis. 27…”

Section: Resultsmentioning

confidence: 99%

Segmentation procedures for the assessment of paranasal sinuses volumes

et al. 2020

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“…49 Recent segmentation studies start using HD95 that measures 95% quantile of distance. [50][51][52][53][54] Its value quantifies the large segmentation error and are closer to the intuition on the contour discrepancy. Futures studies from other groups that include HD95 results might help for justification.…”

Section: Discussionmentioning

confidence: 99%

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

Lei

He²,

Yao

et al. 2020

Medical Physics

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Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer‐aided diagnosis (CAD). This work aims to develop a deep learning‐based method for breast tumor segmentation using three‐dimensional (3D) ABUS automatically. Methods For breast tumor segmentation in ABUS, we developed a Mask scoring region‐based convolutional neural network (R‐CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R‐CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross‐validation and 30 were used for hold‐out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland–Altman analysis. Results The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross‐validation and hold‐out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([−0.77, 1.71)) for the cross‐validation and 0.23 cc ([−0.23 0.69]) for hold‐out test, respectively. Conclusion We developed a novel Mask scoring R‐CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning‐based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

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“…All T2 examinations before and after moisturization were treated in the same way: manual measurements for obtaining T2 values of SC, epidermis and dermis on both exams, and thickness of SC and epidermis on skins examinations before moisturization. Registration was performed between each patient examinations (image after moisturization was registered with the first acquisition), and dice index and Hausdorff distance were obtained for both types of semiautomatic and automatic methods (only evaluating the contour of the skin layers) . The segmentation was done on each examination before and after moisturization.…”

Section: Methodsmentioning

confidence: 99%

“…Registration was performed between each patient examinations (image after moisturization was registered with the first acquisition), and dice index and Hausdorff distance were obtained for both types of semiautomatic and automatic methods (only evaluating the contour of the skin layers). 19,20 The segmentation was done on each examination before and after moisturization. Semiautomatic measurements obtained T2 values of SC, epidermis and dermis on both exams.…”

Section: Course Of the Studymentioning

confidence: 99%

Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

Mesrar

Benhoumich

Miséry

et al. 2019

Skin Research and Technology

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Introduction Previous studies have demonstrated the feasibility to explore moisturization with quantification imaging based on T2 mapping. The aim of this study was to describe and validate the first robust automated method to segment the first layers of the skin. Materials and Methods Data were picked from a previous study that included 35 healthy subjects who underwent a 3T MRI (multi spin echo calculation T2‐weighted sequence) with a microscopic coil on the left heel before and one hour after moisturization. The automatic algorithm was composed of the T2 map generation, a Canny filter, a selection of boundaries, and a local regression to delimitate stratum corneum, epidermis, and dermis. An automated affine registration was applied between the exams before and after moisturization. Results The failure rate of the algorithm was below 5%. Mean computation time was 139.12s. There was a significant and strong correlation between the automatic measurements and the manual ones for the T2 values (ρ: 0.905, P < 0.001) and for the thickness measurements (ρ: 0.8663; P < 0.001). For registration, mean of the Dice index was 0.64 [0.47; 0.80] and of the Hausdorff distance was 0.29 mm 95% CI: [0.28; 0.30]. Conclusion The proposed automatic method to study the first skin layers in 3T MRI using micro‐coils was robust and described T2 values and thickness measurements with a strong correlation to manual measurements. The use of an automated affine registration could also permit the generation of a mapping for a visual assessment of moisturization.

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Accuracy and precision of manual segmentation of the maxillary sinus in MR images—a method study

Cited by 21 publications

References 34 publications

Segmentation procedures for the assessment of paranasal sinuses volumes

Segmentation procedures for the assessment of paranasal sinuses volumes

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

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