Scale-based landmark for internal organs assessments of common carp (Cyprinus carpio) during digital radiography and ultrasonography imaging.

Medical imaging techniques such as digital radiography and ultrasonography are non-invasive and provide precise results for examining internal organs and structures within fish. Their effectiveness can be further enhanced by using body parts like scales as markers for the organs beneath them. This study utilized the number of scales as landmarks in digital radiography and ultrasonography to non-invasively evaluate the muscles, bones, and images of internal and reproductive organs of common carp (Cyprinus carpio). Digital radiography was performed in the dorsoventral and lateral views of the fish, whereas ultrasonography was conducted in longitudinal and transverse views on sequence scale numbers with brightness and colour Doppler-modes. Digital radiography of the common carp revealed the whole-body morphology, including the bony parts from the head, pectoral fins, dorsal fins, pelvic fins, anal fins, and vertebrae to the tail that appeared radiopaque. Internal organs were also observed, with the swim bladder and heart appeared radiolucent, while the intestines, liver, testes, and ovaries appeared radiopaque. Ultrasonography in brightness mode displayed the digestive organs, reproductive organs, and muscle thickness. Additionally, colour Doppler mode demonstrated blood flow within the heart's ventricle.

Reliability of a face scanner in measuring the vertical dimension of occlusion.

OBJECTIVE: This study evaluated the reliability of a face scanner in measuring the vertical dimension of occlusion (VDO). METHODS: Fully dentate volunteers (n = 20; mean-age = 30.0 ± 10.7 years) were recruited. Clinical facial measurements were obtained using a digital caliper and a face scanner (Obiscanner, Fifthingenium, Italy). The scans were imported into a mesh-processing software, and the distances were measured digitally. Measurements were obtained for each participant with the jaws positioned in maximal intercuspation (MI) and with increased vertical distances of 2, 4, and 6 mm. Vertical and horizontal measures were obtained using facial anatomical landmarks: Glabella (GL), Pronasale (PrN), Subnasale (SbN), inferior border of the right and left Alare, Labiale superius (Ls), right and left Cheilion (Ch), Soft Pogonion (SPg), right and left Tragus of the ear (Tr), for all selected vertical positions. Data analysis included intra-class correlation coefficient (ICC), pairwise comparison tests, Bland-Altman plots, and Passing-Bablok regression. RESULTS: 120 VDO measurements (clinical=60, digital=60) were recorded by two independent evaluators. Mean differences between digital and clinical measurements ranged from 0.054 ± 0.14 mm to 0.203 ± 0.13 mm. All parameters were strongly correlated (r > 0.93; p < 0.001). ICC estimates revealed excellent reliability, and the measuring procedure yielded the same results on repeated trials irrespective of the raters and measurement methods. Bland-Altman plots revealed a difference, between digital and clinical measurements, of 1.7 % for the vertical measurements. Regression analysis revealed no significant proportional difference between the two methods, so both can be used interchangeably. CONCLUSIONS: The findings of this study demonstrate that VDO can be measured accurately from face scans using 3D mesh-processing software and that even small changes in the VDO could be detected using the digital methods. CLINICAL SIGNIFICANCE: Findings provide evidence about the reliability of a digital method for jaw relation registrations and may be applied towards incorporating this method into clinical workflows for computer-aided-design/ computer-assisted-manufacturing (CAD-CAM) dentures.

The Posterior Auricularis Muscle as Superficial Landmark for the Sigmoid Sinus and Transverse-Sigmoid Sinus Junction: An Anatomical Study.

OBJECTIVE: Neuronavigation systems coupled with previously reported external anatomical landmarks assist neurosurgeons during intracranial procedures. We aimed to verify whether the posterior auricularis muscle (PAM) could be used as an external landmark for identifying the sigmoid sinus (SS) and the transverse-sigmoid sinus junction (TSSJ) during posterior cranial fossa surgery. METHODS: The PAM was dissected in 10 adult cadaveric heads and after drilling the underlying bone, the relationships with the underlying SS and TSSJ were noted. The width and length of the PAM, and the distance between the muscle and reference points (asterion, mastoid tip, and midline), were measured. RESULTS: The PAM was identified in 18 sides (9 left, 9 right). The first 20 mm of the muscle length (mean 28.28 mm) consistently overlay the mastoid process anteriorly and the proximal half of the SS slightly posteriorly on all sides. The superior border was a mean of 2.22 mm inferior to the TSSJ and, especially when the muscle length exceeded 20 mm, this border extended closer to the transverse sinus; it was usually found at a mean of 3.11 mm (range 0.0-13.80 mm) inferior to the distal third of the transverse sinus. CONCLUSIONS: Superficial landmarks give surgeons improved surgical access, avoiding overexposure of deep neurovascular structures and reducing brain retraction. On the basis of our cadaveric study, the PAM is a reliable and accurate direct landmark for identifying the SS and TSSJ. The PAM could potentially be used for guiding the retrosigmoid approach.

[Study on the method of automatically determining maxillary complex landmarks based on non-rigid registration algorithms].

Objective: To explore an automatic landmarking method for anatomical landmarks in the three-dimensional (3D) data of the maxillary complex and preliminarily evaluate its reproducibility and accuracy. Methods: From June 2021 to December 2022, spiral CT data of 31 patients with relatively normal craniofacial morphology were selected from those who visited the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The sample included 15 males and 16 females, with the age of (33.3±8.3) years. The maxillary complex was reconstructed in 3D using Mimics software, and the resulting 3D data of the maxillary complex was mesh-refined using Geomagic software. Two attending physicians and one associate chief physician manually landmarked the 31 maxillary complex datasets, determining 24 anatomical landmarks. The average values of the three expert landmarking results were used as the expert-defined landmarks. One case that conformed to the average 3D morphological characteristics of healthy individuals' craniofacial bones was selected as the template data, while the remaining 30 cases were used as target data. The open-source MeshMonk program (a non-rigid registration algorithm) was used to perform an initial alignment of the template and target data based on 4 landmarks (nasion, left and right zygomatic arch prominence, and anterior nasal spine). The template data was then deformed to the shape of the target data using a non-rigid registration algorithm, resulting in the deformed template data. Based on the unchanged index property of homonymous landmarks before and after deformation of the template data, the coordinates of each landmark in the deformed template data were automatically retrieved as the automatic landmarking coordinates of the homonymous landmarks in the target data, thus completing the automatic landmarking process. The automatic landmarking process for the 30 target data was repeated three times. The root-mean-square distance (RMSD) of the dense corresponding point pairs (approximately 25 000 pairs) between the deformed template data and the target data was calculated as the deformation error of the non-rigid registration algorithm, and the intra-class correlation coefficient (ICC) of the deformation error in the three repetitions was analyzed. The linear distances between the automatic landmarking results and the expert-defined landmarks for the 24 anatomical landmarks were calculated as the automatic landmarking errors, and the ICC values of the 3D coordinates in the three automatic landmarking repetitions were analyzed. Results: The average three-dimensional deviation (RMSD) between the deformed template data and the corresponding target data for the 30 cases was (0.70±0.09) mm, with an ICC value of 1.00 for the deformation error in the three repetitions of the non-rigid registration algorithm. The average automatic landmarking error for the 24 anatomical landmarks was (1.86±0.30) mm, with the smallest error at the anterior nasal spine (0.65±0.24) mm and the largest error at the left oribital (3.27±2.28) mm. The ICC values for the 3D coordinates in the three automatic landmarking repetitions were all 1.00. Conclusions: This study established an automatic landmarking method for three-dimensional data of the maxillary complex based on a non-rigid registration algorithm. The accuracy and repeatability of this method for landmarking normal maxillary complex 3D data were relatively good.

An automatic facial landmarking for children with rare diseases.

Two to three thousand syndromes modify facial features: their screening requires the eye of an expert in dysmorphology. A widely used tool in shape characterization is geometric morphometrics based on landmarks, which are precise and reproducible anatomical points. Landmark positioning is user dependent and time consuming. Many automatic landmarking tools are currently available but do not work for children, because they have mainly been trained using photographic databases of healthy adults. Here, we developed a method for building an automatic landmarking pipeline for frontal and lateral facial photographs as well as photographs of external ears. We evaluated the algorithm on patients diagnosed with Treacher Collins (TC) syndrome as it is the most frequent mandibulofacial dysostosis in humans and is clinically recognizable although highly variable in severity. We extracted photographs from the photographic database of the maxillofacial surgery and plastic surgery department of Hôpital Necker-Enfants Malades in Paris, France with the diagnosis of TC syndrome. The control group was built from children admitted for craniofacial trauma or skin lesions. After testing two methods of object detection by bounding boxes, a Haar Cascade-based tool and a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based tool, we evaluated three different automatic annotation algorithms: the patch-based active appearance model (AAM), the holistic AAM, and the constrained local model (CLM). The final error corresponding to the distance between the points placed by automatic annotation and those placed by manual annotation was reported. We included, respectively, 1664, 2044, and 1375 manually annotated frontal, profile, and ear photographs. Object recognition was optimized with the Faster R-CNN-based detector. The best annotation model was the patch-based AAM (p < 0.001 for frontal faces, p = 0.082 for profile faces and p < 0.001 for ears). This automatic annotation model resulted in the same classification performance as manually annotated data. Pretraining on public photographs did not improve the performance of the model. We defined a pipeline to create automatic annotation models adapted to faces with congenital anomalies, an essential prerequisite for research in dysmorphology.

[Automatic determination of mandibular landmarks based on three-dimensional mandibular average model].

OBJECTIVE: To explore an efficient and automatic method for determining the anatomical landmarks of three-dimensional(3D) mandibular data, and to preliminarily evaluate the performance of the method. METHODS: The CT data of 40 patients with normal craniofacial morphology were collected (among them, 30 cases were used to establish the 3D mandibular average model, and 10 cases were used as test datasets to validate the performance of this method in determining the mandibular landmarks), and the 3D mandibular data were reconstructed in Mimics software. Among the 40 cases of mandibular data after the 3D reconstruction, 30 cases that were more similar to the mean value of Chinese mandibular features were selected, and the size of the mandibular data of 30 cases was normalized based on the Procrustes analysis algorithm in MATLAB software. Then, in the Geomagic Wrap software, the 3D mandibular average shape model of the above 30 mandibular data was constructed. Through symmetry processing, curvature sampling, index marking and other processing procedures, a 3D mandible structured template with 18 996 semi-landmarks and 19 indexed mandibular anatomical landmarks were constructed. The open source non-rigid registration algorithm program Meshmonk was used to match the 3D mandible template constructed above with the tested patient's 3D mandible data through non-rigid deformation, and 19 anatomical landmark positions of the patient's 3D mandible data were obtained. The accuracy of the research method was evaluated by comparing the distance error of the landmarks manually marked by stomatological experts with the landmarks marked by the method of this research. RESULTS: The method of this study was applied to the data of 10 patients with normal mandibular morphology. The average distance error of 19 landmarks was 1.42 mm, of which the minimum errors were the apex of the coracoid process [right: (1.01±0.44) mm; left: (0.56±0.14) mm] and maximum errors were the anterior edge of the lowest point of anterior ramus [right: (2.52±0.95) mm; left: (2.57±1.10) mm], the average distance error of the midline landmarks was (1.15±0.60) mm, and the average distance error of the bilateral landmarks was (1.51±0.67) mm. CONCLUSION: The automatic determination method of 3D mandibular anatomical landmarks based on 3D mandibular average shape model and non-rigid registration algorithm established in this study can effectively improve the efficiency of automatic labeling of 3D mandibular data features. The automatic determination of anatomical landmarks can basically meet the needs of oral clinical applications, and the labeling effect of deformed mandible data needs to be further tested.

Automatic determination of mandibular landmarks based on three-dimensional mandibular average model / 北京大学学报(医学版)

OBJECTIVE@#To explore an efficient and automatic method for determining the anatomical landmarks of three-dimensional(3D) mandibular data, and to preliminarily evaluate the performance of the method.@*METHODS@#The CT data of 40 patients with normal craniofacial morphology were collected (among them, 30 cases were used to establish the 3D mandibular average model, and 10 cases were used as test datasets to validate the performance of this method in determining the mandibular landmarks), and the 3D mandibular data were reconstructed in Mimics software. Among the 40 cases of mandibular data after the 3D reconstruction, 30 cases that were more similar to the mean value of Chinese mandibular features were selected, and the size of the mandibular data of 30 cases was normalized based on the Procrustes analysis algorithm in MATLAB software. Then, in the Geomagic Wrap software, the 3D mandibular average shape model of the above 30 mandibular data was constructed. Through symmetry processing, curvature sampling, index marking and other processing procedures, a 3D mandible structured template with 18 996 semi-landmarks and 19 indexed mandibular anatomical landmarks were constructed. The open source non-rigid registration algorithm program Meshmonk was used to match the 3D mandible template constructed above with the tested patient's 3D mandible data through non-rigid deformation, and 19 anatomical landmark positions of the patient's 3D mandible data were obtained. The accuracy of the research method was evaluated by comparing the distance error of the landmarks manually marked by stomatological experts with the landmarks marked by the method of this research.@*RESULTS@#The method of this study was applied to the data of 10 patients with normal mandibular morphology. The average distance error of 19 landmarks was 1.42 mm, of which the minimum errors were the apex of the coracoid process [right: (1.01±0.44) mm; left: (0.56±0.14) mm] and maximum errors were the anterior edge of the lowest point of anterior ramus [right: (2.52±0.95) mm; left: (2.57±1.10) mm], the average distance error of the midline landmarks was (1.15±0.60) mm, and the average distance error of the bilateral landmarks was (1.51±0.67) mm.@*CONCLUSION@#The automatic determination method of 3D mandibular anatomical landmarks based on 3D mandibular average shape model and non-rigid registration algorithm established in this study can effectively improve the efficiency of automatic labeling of 3D mandibular data features. The automatic determination of anatomical landmarks can basically meet the needs of oral clinical applications, and the labeling effect of deformed mandible data needs to be further tested.

Study on the method of automatically determining maxillary complex landmarks based on non-rigid registration algorithms / 中华口腔医学杂志

Objective: To explore an automatic landmarking method for anatomical landmarks in the three-dimensional (3D) data of the maxillary complex and preliminarily evaluate its reproducibility and accuracy. Methods: From June 2021 to December 2022, spiral CT data of 31 patients with relatively normal craniofacial morphology were selected from those who visited the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The sample included 15 males and 16 females, with the age of (33.3±8.3) years. The maxillary complex was reconstructed in 3D using Mimics software, and the resulting 3D data of the maxillary complex was mesh-refined using Geomagic software. Two attending physicians and one associate chief physician manually landmarked the 31 maxillary complex datasets, determining 24 anatomical landmarks. The average values of the three expert landmarking results were used as the expert-defined landmarks. One case that conformed to the average 3D morphological characteristics of healthy individuals' craniofacial bones was selected as the template data, while the remaining 30 cases were used as target data. The open-source MeshMonk program (a non-rigid registration algorithm) was used to perform an initial alignment of the template and target data based on 4 landmarks (nasion, left and right zygomatic arch prominence, and anterior nasal spine). The template data was then deformed to the shape of the target data using a non-rigid registration algorithm, resulting in the deformed template data. Based on the unchanged index property of homonymous landmarks before and after deformation of the template data, the coordinates of each landmark in the deformed template data were automatically retrieved as the automatic landmarking coordinates of the homonymous landmarks in the target data, thus completing the automatic landmarking process. The automatic landmarking process for the 30 target data was repeated three times. The root-mean-square distance (RMSD) of the dense corresponding point pairs (approximately 25 000 pairs) between the deformed template data and the target data was calculated as the deformation error of the non-rigid registration algorithm, and the intra-class correlation coefficient (ICC) of the deformation error in the three repetitions was analyzed. The linear distances between the automatic landmarking results and the expert-defined landmarks for the 24 anatomical landmarks were calculated as the automatic landmarking errors, and the ICC values of the 3D coordinates in the three automatic landmarking repetitions were analyzed. Results: The average three-dimensional deviation (RMSD) between the deformed template data and the corresponding target data for the 30 cases was (0.70±0.09) mm, with an ICC value of 1.00 for the deformation error in the three repetitions of the non-rigid registration algorithm. The average automatic landmarking error for the 24 anatomical landmarks was (1.86±0.30) mm, with the smallest error at the anterior nasal spine (0.65±0.24) mm and the largest error at the left oribital (3.27±2.28) mm. The ICC values for the 3D coordinates in the three automatic landmarking repetitions were all 1.00. Conclusions: This study established an automatic landmarking method for three-dimensional data of the maxillary complex based on a non-rigid registration algorithm. The accuracy and repeatability of this method for landmarking normal maxillary complex 3D data were relatively good.

A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography.

Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the input data and limited amount of training data due to the ethical restrictions on the use of medical data. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed. The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. Finally, coarse-to-fine detection is applied to the small bounding box provided by rough estimation, using separate strategies suitable for the mandible and the cranium. For mandibular landmarks, patch-based 3D CNN is applied to the segmented image of the mandible (separated from the maxilla), in order to capture 3D morphological features of mandible associated with the landmarks. We detect 6 landmarks around the condyle all at once rather than one by one, because they are closely related to each other. For cranial landmarks, we again use the VAE-based latent representation for more accurate annotation. In our experiment, the proposed method achieved a mean detection error of 2.88 mm for 90 landmarks using only 15 paired training data.

Breast Anthropometry: Values and Application in Breast Surgery for Vietnamese Women.

OBJECTIVES: Breast surgery requires a high aesthetic outcome and should be individualized according to anthropometric breast and body characteristics. This study aimed to measure the anthropometric parameters and volume of Vietnamese female breasts and their application in breast surgery. SUBJECTS AND METHODS: A cross-sectional descriptive study enrolled 240 women treated at Vietnam National Cancer Hospital aged 18 to 78 years old. The measurements were obtained with the patient sitting upright in the anatomic position based on key landmarks and breast volume was also assessed. Differences in breast anthropometric measurements and breast volume were compared between groups of age, BMI, and the number of children. The correlation between breast volume calculated by anthropometric method and surgical specimen volume was evaluated to determine the accuracy of this method. RESULTS: The mean values of the right and left breast volumes are less statistically different. Mean breast volume of the right breast and left breast were 396.1±182.3ml and 399.4±182.2ml, respectively. The proportion of breast ptosis increased with age (p=0.027), Body mass index (p<0.0001), and the number of children (p=0.004). The most important factor affecting the size and shape of the breast was body mass index (BMI). Mastectomy specimen volume and breast volume calculated by the anthropometric method are highly correlated with r=0.966. CONCLUSIONS: The results of this study should be applied in clinical practice in breast surgery for Vietnamese women.
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The Intersection Between the Sternocleidomastoid and Splenius Capitis as the Anatomical Landmark to Facilitate Occipital Artery Harvest: A Retrospective Clinical Study.

BACKGROUND: Occipital artery (OA)-posterior inferior cerebellar artery (PICA) bypass is a challenging procedure and is not frequently performed owing to the difficulty of OA harvest. To facilitate harvest, the intersection between the sternocleidomastoid and splenius capitis (the OA triangle) is used as the anatomical landmark to identify the OA segment that carries the highest risk of damage. This clinical study aimed to demonstrate efficacy and safety of OA harvest using this landmark. METHODS: The study included 18 patients who underwent OA harvest using the OA triangle as a landmark for treatment of vertebral artery and PICA aneurysms. Patients were retrospectively evaluated for safety and patency of OA after harvest and OA-PICA bypass. RESULTS: Of 18 patients with ruptured and unruptured vertebral artery and PICA aneurysms, 13 (72.2%) underwent OA-PICA bypass and 5 (27.8%) did not undergo bypass. The OA was completely harvested without damage in all patients. After harvest, the OA was patent in 17 patients (94.4%) and was occluded in 1 patient owing to vasospasm; this patient then underwent recanalization resulting in good patency of the OA-PICA bypass. The patency rate of the OA-PICA bypass was 100%. CONCLUSIONS: The OA triangle, which is the anatomical landmark of the proximal end of the transitional segment of the OA, facilitated OA harvest using the distal-to-proximal harvest technique with safety and good patency. To the best of our knowledge, this is the first study of OA harvest in clinical cases.

The radiographic relationship between the cortical overlap view (COV) and the tip of the greater trochanter.

For proximal femoral nailing, choosing the proper entry point with the aid of C-arm imaging is crucial. Therefore, obtaining accurate radiological views that facilitate sound identification of the tip of the greater trochanter (GT) is of utmost importance. The aim of this study was to define a radiological view characterised by reproducible radiographic landmarks which will allow the reliable identification of the tip of the GT in the anteroposterior view. Anatomical and radiographic features of 16 cadaveric femurs were analysed. The cortical overlap view (COV), characterised by the radiological overlap of the density line of the piriform fossa and the intertrochanteric crest, was identified. It marks the rotation of the proximal femur at which the GT can be accurately identified and used to determine the desired entry point for a proximal femoral nail. Trainees and fully qualified orthopedic trauma surgeons were asked to identify the correct COV in radiological imaging series. Mean internal rotation of the femur to achieve a COV was 17.5° (range 12.8°-21.8°). In the COV the tip of the GT is the highest visible point and the mean distance from the cortical overlap line to the tip of the GT is 4.45 mm. Intra- and inter-rater reliability was high with ICC(2,k) = 0.932 and ICC(2,k) = 0.987 respectively. Trainees achieved higher rates of correct COV identification than specialists. There was no significant correlation between the internal rotation of the femur to achieve the COV and femoral antetorsion. In conclusion, the COV is a highly reproducible radiological view that is characterised by radiographic landmarks easy to recognise. It allows for accurate identification of the tip of the GT, which can be used by the surgeon as a reference to determine the desired entry point for an intramedullary nail.

Meniscus Anatomy and Basic Science.

A basic understanding of meniscal anatomy and biomechanics is important for physicians evaluating knee injuries and surgeons treating meniscal injuries. This chapter provides a concise review of meniscal anatomy and biomechanics relevant for the evaluation and treatment of meniscus injuries. Anatomic landmarks relevant for meniscal root repair and transplant are discussed, along with the gross, microscopic, vascular, and neuroanatomy of the menisci.

Anthropometric measurements of patellar ridge using computed tomography-based three-dimensional computer models.

BACKGROUND: The objectives of this study were to investigate the anatomic morphology of patellar ridge using computed tomography-based three-dimensional (3D) computer models and to assess the center of the patellar ridge after virtual resections. METHODS: We selected 80 patients, 40 males (age, 33.2±6.8 years) and 40 females (age, 30.6±7.2 years), who were slightly symptomatic with soft tissue injury of the knee joint. The right or left knees were scanned by computed tomography (CT). The CT data of 160 knees was used to construct 3D computer models by image analysis software (Mimics). Variables such as the angle between the patellar ridge and patellar long axis, the distance between the center of the patellar ridge and the center of patellar cut after virtual resections were measured. We detect differences between the sides and genders with the 3D computer models by Student's t test. Simple linear regression and correlation test was used to correlate the patellar ridge center to the center of the patellar cut. RESULTS: According to the available data, there were significant gender differences in the length and width of patellar cut after virtual resections even with strict control for the height and weight of the patients. The angle between the patellar ridge and the patellar long axis was 11.24° ± 3.62°. The angle in male patients was 10.17° ± 4.82°, and it was 12.28°± 3.78° in female patients. The morphological difference was statistically significant (P < 0.05). After using the subchondral method to virtually resect the patellae, with reference to the center of the patellar cut, the center of the patellar ridge lies superiorly and medially in 88.75%, inferiorly and medially in 8.75%, laterally and superiorly in 2.5%, and in no case laterally and inferiorly. The intra-observer reliability regarding the dimensional measurements was excellent in this study. CONCLUSIONS: Advances in 3D computer models had resulted in the availability of preoperative measurement and virtual planning. The anthropometric dimensions of this study could provide general information for guiding surgical management of the patella in total knee arthroplasty (TKA) and were useful in designing patellar implants. CLINICAL RELEVANCE: The placement of the patellar component during TKA differs from one patella to another. The anatomic morphology information of the patellar ridge is helpful for surgeons to perform patellar resurfacing in TKA.

Structure-Coherent Deep Feature Learning for Robust Face Alignment.

In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. The model and code are publicly available at https://github.com/BeierZhu/Sturcture-Coherency-Face-Alignment.

Segment-Specific Orientation of the Dorsal and Ventral Roots for Precise Therapeutic Targeting of Human Spinal Cord.

OBJECTIVE: To provide precise description of the dorsal and ventral roots orientation along with the main spinal cord anatomical measurements and their segment-specific variations. PATIENTS AND METHODS: We collected and analyzed the measurements of the spines, spinal cords, and dorsal and ventral roots (C2-L5) of nine adult cadavers (five males and four females). RESULTS: This study for the first time provides analysis of the dorsal and ventral roots orientation along with spinal cord anatomical measurements and their segment-specific distribution. The results of this study showed less variability in rostral root angles compared with the caudal. Dorsal and ventral rootlets were oriented mostly perpendicular to the spinal cord at the cervical level and had more parallel orientation to the spinal cord at the thoracic and lumbar segments. The number of rootlets per root was greatest at dorsal cervical and lumbar segments. Spinal cord transverse diameter and width of the dorsal columns were largest at cervical segments. The strongest correlation between the spinal cord and vertebrae structures was found between the length of intervertebral foramen to rostral rootlet distance and vertebral bone length. CONCLUSION: These results demonstrate consistent variation in spinal cord anatomical features across all tested subjects. The results of this study can be used to locate spinal roots and main spinal cord landmarks based on bone marks on computed tomography or X-rays. These results could improve stereotactic surgical procedures and electrode positioning for neuromodulation procedures.

The Inferolateral Surgical Triangle of the Cavernous Sinus: A Cadaveric and MRI Study with Neurosurgical Significance.

BACKGROUND: The inferolateral triangle is a surgical skull base triangle used as a neurosurgical landmark. There are few reports of its measurements with little attention paid to anatomic variations. METHODS: The inferolateral triangle was measured in 10 adult human cadaveric heads via dissection then direct measurement and 5 participants undergoing neuroimaging using tracing features. RESULTS: In the cadavers, mean lengths (mm) of the superior, anterior, and posterior borders were 17.0 (±5.5), 12.9 (±1.7), and 17.8 (±3.3), respectively, with mean area of 97.85 (±28.17) mm2. In the participants, mean lengths (mm) of the superior, anterior, and posterior borders were 17.35 (±4.01), 14.36 (±1.36), and 18.01 (±2.43), respectively, with mean area of 113.6 (±25.46) mm2. No statistical difference in triangle areas between groups was found. CONCLUSIONS: Intimate understanding of the inferolateral triangle is essential to skull-based surgery; knowing its anatomy and variations aids in surgical planning and understanding of regional pathology.

Comparing semi-landmarking approaches for analyzing three-dimensional cranial morphology.

OBJECTIVES: Increased use of three-dimensional (3D) imaging data has led to a need for methods capable of capturing rich shape descriptions. Semi-landmarks have been demonstrated to increase shape information but placement in 3D can be time consuming, computationally expensive, or may introduce artifacts. This study implements and compares three strategies to more densely sample a 3D image surface. MATERIALS AND METHODS: Three dense sampling strategies: patch, patch-thin-plate spline (TPS), and pseudo-landmark sampling, are implemented to analyze skulls from three species of great apes. To evaluate the shape information added by each strategy, the semi or pseudo-landmarks are used to estimate a transform between an individual and the population average template. The average mean root squared error between the transformed mesh and the template is used to quantify the success of the transform. RESULTS: The landmark sets generated by each method result in estimates of the template that on average were comparable or exceeded the accuracy of using manual landmarks alone. The patch method demonstrates the most sensitivity to noise and missing data, resulting in outliers with large deviations in the mean shape estimates. Patch-TPS and pseudo-landmarking provide more robust performance in the presence of noise and variability in the dataset. CONCLUSIONS: Each landmarking strategy was capable of producing shape estimations of the population average templates that were generally comparable to manual landmarks alone while greatly increasing the density of the shape information. This study highlights the potential trade-offs between correspondence of the semi-landmark points, consistent point spacing, sample coverage, repeatability, and computational time.

Three-dimensional surface scanning methods in osteology: A topographical and geometric morphometric comparison.

OBJECTIVES: Three-dimensional (3D) data collected by structured light scanners, photogrammetry, and computed tomography (CT) scans are increasingly combined in joint analyses, even though the scanning techniques and reconstruction software differ considerably. The aim of the present study was to compare the quality and accuracy of surface models and landmark data obtained from modern clinical CT scanning, 3D structured light scanner, photogrammetry, and MicroScribe digitizer. MATERIAL AND METHODS: We tested 13 different photogrammetric software tools and compared surface models obtained by different methods for four articulated human pelves in a topographical analysis. We also measured a set of 219 landmarks and semilandmarks twice on every surface as well as directly on the dry bones with a MicroScribe digitizer. RESULTS: Only one photogrammetric software package yielded surface models of the complete pelves that could be used for further analysis. Despite the complex pelvic anatomy, all three methods (CT scanning, 3D structured light scanning, photogrammetry) yielded similar surface representations with average deviations among the surface models between 100 and 200 µm. A geometric morphometric analysis of the measured landmarks showed that the different scanning methods yielded similar shape variables, but data acquisition via MicroScribe digitizer was most prone to error. DISCUSSION: We demonstrated that three-dimensional models obtained by different methods can be combined in a single analysis. Photogrammetry proved to be a cheap, quick, and accurate method to generate 3D surface models at useful resolutions, but photogrammetry software packages differ enormously in quality.