Biomedical Physics & Engineering Express最新文献

{"title":"Residual Pix2Pix networks: streamlining PET/CT imaging process by eliminating CT energy conversion.","authors":"S Ghanbari, A Sadremomtaz","doi":"10.1088/2057-1976/ad97c2","DOIUrl":"10.1088/2057-1976/ad97c2","url":null,"abstract":"<p><p>Attenuation correction of PET data is commonly conducted through the utilization of a secondary imaging technique to produce attenuation maps. The customary approach to attenuation correction, which entails the employment of CT images, necessitates energy conversion. However, the present study introduces a novel deep learning-based method that obviates the requirement for CT images and energy conversion. This study employs a residual Pix2Pix network to generate attenuation-corrected PET images using the 4033 2D PET images of 37 healthy adult brains for train and test. The model, implemented in TensorFlow and Keras, was evaluated by comparing image similarity, intensity correlation, and distribution against CT-AC images using metrics such as PSNR and SSIM for image similarity, while a 2D histogram plotted pixel intensities. Differences in standardized uptake values (SUV) demonstrated the model's efficiency compared to the CTAC method. The residual Pix2Pix demonstrated strong agreement with the CT-based attenuation correction, the proposed network yielding MAE, MSE, PSNR, and MS-SSIM values of 3 × 10^-3, 2 × 10^-4, 38.859, and 0.99, respectively. The residual Pix2Pix model's results showed a negligible mean SUV difference of 8 × 10^-4(P-value = 0.10), indicating its accuracy in PET image correction. The residual Pix2Pix model exhibits high precision with a strong correlation coefficient of R² = 0.99 to CT-based methods. The findings indicate that this approach surpasses the conventional method in terms of precision and efficacy. The proposed residual Pix2Pix framework enables accurate and feasible attenuation correction of brain F-FDG PET without CT. However, clinical trials are required to evaluate its clinical performance. The PET images reconstructed by the framework have low errors compared to the accepted test reliability of PET/CT, indicating high quantitative similarity.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration.","authors":"Zhehao Zhang, Yao Hao, Xiyao Jin, Deshan Yang, Ulugbek S Kamilov, Geoffrey D Hugo","doi":"10.1088/2057-1976/ad97c1","DOIUrl":"10.1088/2057-1976/ad97c1","url":null,"abstract":"<p><p><i>Objective</i>. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible. This work aims to improve the efficiency of 4D-CBCT MoCo reconstruction using DL-based registration for the rapid generation of a motion model prior to treatment.<i>Approach.</i>An artifact-reduction DL model was first used to improve the initial 4D-CBCT reconstruction by reducing streaking artifacts. Based on the artifact-reduced phase images, a groupwise DIR employing DL was used to estimate the inter-phase motion model. Two DL DIR models using different learning strategies were employed: (1) a patient-specific one-shot DIR model which was trained from scratch only using the images to be registered, and (2) a population DIR model which was pre-trained using collected 4D-CT images from 35 patients. The registration accuracy of two DL DIR models was assessed and compared to a conventional groupwise DIR approach implemented in the Elastix toolbox using the publicly available DIR-Lab dataset, a Monte Carlo simulation dataset from the SPARE challenge, and two clinical cases.<i>Main results.</i>The patient-specific DIR model and the population DIR model demonstrated registration accuracy comparable to the conventional state-of-the-art methods on the DIR-Lab dataset. No significant difference in image quality was observed between the final MoCo reconstructions using the patient-specific model and population model for motion modeling, compared to using the conventional approach. The average runtime (hh:mm:ss) of the entire MoCo reconstruction on SPARE dataset was reduced from 01:37:26 using conventional DIR method to 00:10:59 using patient-specific model and 00:01:05 using the pre-trained population model.<i>Significance.</i>DL-based registration methods can improve the efficiency in generating motion models for 4D-CBCT without compromising the performance of final MoCo reconstruction.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of organs dosimetry precision using ATOM phantom and optically stimulated luminescence detectors in computed tomography.","authors":"Abdellah Khallouqi, Hamza Sekkat, Omar El Rhazouani, Abdellah Halimi","doi":"10.1088/2057-1976/ad992e","DOIUrl":"10.1088/2057-1976/ad992e","url":null,"abstract":"<p><p>The primary objective of this study was to compare organ doses measured using optically stimulated luminescent dosimeters (OSLDs) with those estimated by the CT-EXPO software for common CT protocols. An anthropomorphic ATOM phantom was employed to measure organ doses across head, chest, and abdominal CT scans performed on a Hitachi Supria 16-slice CT scanner. These OSLD measurements were then compared to the estimates provided by the widely used CT-EXPO software. Organ doses were assessed using OSLDs placed in an adult anthropomorphic phantom, with calibration performed through a comprehensive process involving multiple tube potentials and sensitivity corrections. Results from three CT acquisitions per protocol were compared to estimates provided by CT-EXPO software. Findings reveal significant discrepancies between measured and estimated organ doses, with p-values consistently below 0.05 across all organs. For head CT, measured eye lens doses averaged 33.51 mGy, 6.0% lower than the estimated 35.65 mGy. In chest CT, the thyroid dose was 9.82 mGy, 13.5% higher than the estimated 8.65 mGy. For abdominal CT, the liver dose measured 12.11 mGy, 9.6% higher than the estimated 11.05 mGy. Measured doses for the rest of organs were generally lower than those predicted by CT-EXPO, showing some limitations in current estimation models and the importance of precise dosimetry. This study highlights the potential of OSLD measurements as a complementary method for organ dose assessment in CT imaging, emphasizing the need for more accurate organ dose measurement to optimize patient care.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of urinary albumin in clinical samples using smartphone enabled LFA reader incorporating automated segmentation.","authors":"Sunita Bhatt, Richa Gupta, Vijay R N Prabhakar, Prashant Kumar Shukla, Sudip Kumar Datta, Satish Kumar Dubey","doi":"10.1088/2057-1976/ad992d","DOIUrl":"10.1088/2057-1976/ad992d","url":null,"abstract":"<p><p>Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-conventional deep brain stimulation in a network model of movement disorders.","authors":"Nada Yousif, Peter G Bain, Dipankar Nandi, Roman Borisyuk","doi":"10.1088/2057-1976/ad9c7d","DOIUrl":"10.1088/2057-1976/ad9c7d","url":null,"abstract":"<p><p>Conventional deep brain stimulation (DBS) for movement disorders is a well-established clinical treatment. Over the last few decades, over 200,000 people have been treated by DBS worldwide for several neurological conditions, including Parkinson's disease and Essential Tremor. DBS involves implanting electrodes into disorder-specific targets in the brain and applying an electric current. Although the hardware has developed in recent years, the clinically used stimulation pattern has remained as a regular frequency square pulse. Recent studies have suggested that phase-locking, coordinated reset or irregular patterns may be as or more effective at desynchronising the pathological neural activity. Such studies have shown efficacy using detailed neuron models or highly simplified networks and considered one frequency band. We previously described a population level model which generates oscillatory activity in both the beta band (20 Hz) and the tremor band (4 Hz). Here we use this model to look at the impact of applying regular, irregular and phase dependent bursts of stimulation, and show how this influences both tremor- and beta-band activity. We found that bursts are as or more effective at suppressing the pathological oscillations compared to continuous DBS. Importantly however, at higher amplitudes we found that the stimulus drove the network activity, as seen previously. Strikingly, this suppression was most apparent for the tremor band oscillations, with beta band pathological activity being more resistant to the burst stimulation compared to continuous, conventional DBS. Furthermore, our simulations showed that phase-locked bursts of stimulation did not convey much improvement on regular bursts of oscillation. Using a genetic algorithm optimisation approach to find the best stimulation parameters for regular, irregular and phase-locked bursts, we confirmed that tremor band oscillations could be more readily suppressed. Our results allow exploration of stimulation mechanisms at the network level to formulate testable predictions regarding parameter settings in DBS.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HeatGSNs: Integrating Eigenfilters and Low-Pass Graph Heat Kernels into Graph Spectral Convolutional Networks for Brain Tumor Segmentation and Classification.","authors":"Jihun Bae, Hunmin Lee, Jinglu Hu","doi":"10.1088/2057-1976/ada1db","DOIUrl":"https://doi.org/10.1088/2057-1976/ada1db","url":null,"abstract":"<p><p>Recent studies on graph representation learning in brain tumor learning tasks have garnered significant interest by encoding and learning inherent relationships among the geometric features of tumors. There are serious class imbalance problems that occur on brain tumor MRI datasets. Impressive deep learning models like CNN- and Transformer-based can easily address this problem through their complex model architectures with large parameters.&#xD;However, graph-based networks are not suitable for this approach because of chronic over-smoothing and oscillation convergence problems. To address these challenges at once, we propose novel graph spectral convolutional networks called HeatGSNs, which incorporate eigenfilters and learnable low-pass graph heat kernels to capture geometric similarities within tumor classes. They operate to a continuous feature propagation mechanism derived by the forward finite difference of graph heat kernels, which is approximated by the cosine form for the shift-scaled Chebyshev polynomial and modified Bessel functions, leading to fast and accurate performance achievement. Our experimental results show a best average Dice score of 90%, an average Hausdorff Distance (95%) of 5.45mm, and an average accuracy of 80.11% in the BRATS2021 dataset. Moreover, HeatGSNs require significantly fewer parameters, averaging 1.79M, compared to other existing methods, demonstrating efficiency and effectiveness.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dose optimization of extended collimators in boron neutron capture therapy.","authors":"Yadi Zhu, Chao Lian, Xiang Ji, Xiaoxiang Zhang, Chunjing Li, Yunqing Bai, Jun Gao","doi":"10.1088/2057-1976/ad9c7f","DOIUrl":"10.1088/2057-1976/ad9c7f","url":null,"abstract":"<p><p>In this paper, we propose the design of extending collimators aimed at reducing the radiation dose received by patients with normal tissues and protecting organs at risk in Boron Neutron Capture Therapy (BNCT). Three types of extended collimators are studied: Type 1, which is a traditional design; Type 2, which is built upon Type 1 by incorporating additional polyethylene material containing lithium fluoride (PE(LiF)); Type 3, which adds lead (Pb) to Type 1. We evaluated the dose distribution characteristics of the above-extended collimators using Monte Carlo methods simulations under different configurations: in air, in a homogeneous phantom, and a humanoid phantom model. Firstly, the neutron and gamma-ray fluxes at the collimator outlet of the three designs showed no significant changes, thus it can be expected that their therapeutic effects on tumors will be similar. Then, the dose distribution outside the irradiation field was studied. The results showed that, compared with Type 1, Type 2 has a maximum reduction of 57.14% in neutron leakage dose, and Type 3 has a maximum reduction of 21.88% in gamma-ray leakage dose. This will help to reduce the radiation dose to the local skin. Finally, the doses of different organs were simulated. The results showed that the neutron dose of Type 2 was relatively low, especially for the skin, thyroid, spinal cord, and left lung, with the neutron dose reduced by approximately 20.34%, 16.18%, 26.05%, and 18.91% respectively compared to Type 1. Type 3 collimator benefits in reducing gamma-ray dose for the thyroid, esophagus, and left lung organs, with gamma-ray dose reductions of around 10.81%, 9.45%, and 10.42% respectively. This indicates that attaching PE(LiF) or Pb materials to a standard collimator can suppress the dose distribution of patient organs, which can provide valuable insights for the design of extended collimators in BNCT.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the SIMIND simulation code using the myocardial phantom HL.","authors":"Yoshiaki Yasumoto, Hiromitsu Daisaki, Mitsuru Sato","doi":"10.1088/2057-1976/ad960d","DOIUrl":"10.1088/2057-1976/ad960d","url":null,"abstract":"<p><p><i>Introduction</i>. Monte Carlo simulation codes simulating medical imaging nuclear detectors (SIMIND) are notable tools used to model nuclear medicine experiments.This study aimed to confirm the usability of SIMIND as an alternative method for nuclear medicine experiments with a cardiac phantom HL, simulating human body structures, by comparing the actual experiment data.<i>Methods</i>. A cardiac phantom HL that simulates myocardial scintigraphy using¹²³I-meta-iodobenzylguanidine was developed, and single-photon emission computed tomography/computed tomography imaging was performed using Discovery NM/CT 670 scanner. Aside from the main-energy window(159 keV ± 10%), additional windows were set on the low(137.5 keV ± 4% ) and high(180.5 keV ± 3%)-energy sides. The simulations were performed under the same conditions as the actual experiments. Regions of interest (ROIs) were set in each organ part of the experiments and simulated data, and a polar map for the myocardial part was developed. The mean, maximum (max), and minimum (min) counts within each ROI, as well as the relative errors of each segment in the polar map, were calculated to evaluate the accuracy of the simulation.<i>Results</i>. Overall, the results were favorable with relative errors of <10% except in some areas based on the data from the main-energy window and postreconstruction. On the other hand, relative errors of >10% were found in both the low and high subenergy windows. The smallest error occurred when assessing using mean values within the ROIs. The relative error was high at the cardiac base in the polar map evaluation; however, it remained <10% from the mid to apical heart sections.<i>Conclusion</i>. SIMIND is considered an alternative method for nuclear medicine experiments using a myocardial phantom HL that closely resembles human body structures. However, caution is warranted as accuracy may decrease under specific conditions.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation of high-Z material for bolus in electron beam therapy.","authors":"Indra J Das, Ahtesham U Khan, Sara Lim, Poonam Yadav, Eric Donnelley, Bharat B Mittal","doi":"10.1088/2057-1976/ad9c7c","DOIUrl":"10.1088/2057-1976/ad9c7c","url":null,"abstract":"<p><p><i>Highlight</i>. Electron beam treatment often requires bolus to augment surface dose to nearly 100%. There are no optimum bolus materials and hence a high-Z based clothlike material is investigated to reduce air column in treatment that provides optimum surface dose. This material is well suited as it can be used multiple times and can be sanitized. Characteristics of W-Si material is provided.<i>Purpose /Objective(s)</i>. Electron beams are frequently used for superficial tumors. However, due to electron beam characteristics the surface dose is 75-95% of the prescribed dose depending on beam energy thus requiring placement of bolus to augment surface dose. Various types of boluses are commonly used in clinics, each having it's own unique limitation. Most bolus devices do not conform to the skin contour and create airgaps that are known to produce dose perturbations creating hot and cold spots. A cloth-like high-Z materials; Tungsten, (Z = 74) and Bismuth, (Z = 83) impregnated in silicone gel is investigated for electron bolus.<i>Materials/Methods</i>. Super soft silicone-gel based submillimeter thin tungsten and bismuth sheets were investigated for bolus for 6-12 MeV. Parallel plate ion chamber measurements were performed in a solid water phantom on a Varian machine. Depth dose characteristics were measured to optimize the thickness for surface dose to be 100% for selected electron therapy and validated with Monte Carlo simulations.<i>Results</i>. Silicone-gel tungsten and bismuth sheets produce significant electrons thus increasing surface dose. Based on measured depth dose, our data showed that tungsten sheets of 0.14 mm, 0.18 mm and 0.2 mm and Bismuth sheets of 0.42 mm, 0.18 mm and 0.2 mm provide 100% surface dose for 6, 9 and 12 MeV beams, respectively without any significant changes in depth dose except increasing surface dose.<i>Conclusions</i>. The new high-Z clothlike sheets are extremely soft but high tensile metallic bolus materials that can fit flawlessly on any skin contour. Only 0.2 mm thick sheets are needed for 100% surface dose without degradation of the depth dose characteristics. These materials are reusable and ideal for bolus in electron beam treatment. This investigation opens a new frontier in designing new bolus materials optimum for patient treatment.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative assessment system for placental gross examination with precise localization of umbilical cord insertion point.","authors":"Yiming Liu, Yuehua Liang, Ting Yu, Xiang Tao, Xin Wu, Yan Wang, Qingli Li","doi":"10.1088/2057-1976/ad98a3","DOIUrl":"10.1088/2057-1976/ad98a3","url":null,"abstract":"<p><p>A quantitative assessment for measuring the placenta during gross examination is a crucial step in evaluating the health status of both the mother and the fetus. However, in the current clinical practice, time-consuming and observer-variant drawbacks are caused due to manual measurement and subjective determination of placental characteristics. Therefore, we propose a quantitative assessment system for placenta gross examination to efficiently and accurately measuring placental characteristics according to Amsterdam Consensus, including weight and thickness of placenta, length and width of placental disc, length and diameter of umbilical cord, distance from umbilical cord insertion point to placental edges, etc. The proposed system consists of (1) an instrument designed for standard acquisition of image, weight and thickness of placenta and (2) an algorithm for quantitative morphological assessment based on precise segmentation of placental disc and umbilical cord and localization of umbilical cord insertion point. Considering the complexity of spatial distribution and ambiguous texture of umbilical cord insertion point, we design Umbilical Cord Insertion Point Candidate Generator to provide reliable umbilical cord insertion point location by employing prior structural knowledge of umbilical cord. Therefore, we integrate the Umbilical Cord Insertion Point Candidate Generator with a Base Detector to ensure umbilical cord insertion point is provided when the Base Detector fails to generate high-scoring candidate points. Experimental results on our self-collected placenta dataset demonstrate the effectiveness of our proposed algorithm. The measurements of placental morphological assessment are calculated based on segmentation and localization results. Our proposed quantitative assessment system, along with its associated instrument and algorithm, can automatically extract numerical measurements to boost the standardization and efficiency of placental gross examination.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}