Surface Point Cloud Ultrasound with Transcranial Doppler: Coregistration of Surface Point Cloud Ultrasound with Magnetic Resonance Angiography for Improved Reproducibility, Visualization, and Navigation in Transcranial Doppler Ultrasound

Stember, Joseph N.; Terilli, Kalijah; Perez, Edgar L; Megjhani, Murad; Cooper, C; Jambawalikar, Sachin; Park, S.

doi:10.1007/s10278-020-00328-y

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Using Philips iU22 color US diagnostic system, with a probe frequency of 3.5 Hz, two US professional deputy chief physicians performed US images to assess whether there was placenta accreta, and the cases diagnosed with placenta accreta were classified according to the previous literature: In Grade I, the retroplacental space disappears and is occupied by placental tissue, the strong echo lines of the decidua disappear, and the myometrium becomes thin (<2 mm). In Grade II, in addition to the manifestations of Grade I, the myometrium disappears, and there are venous pools of different sizes in the placenta, which are the same as the abnormally dilated blood vessels on the uterine wall after the placenta [ 12 ]. In Grade III, in addition to grade II manifestations, the uterine serosa layer is interrupted, local blood vessels and solid mass shadows protrude to the bladder, the posterior wall of the bladder is not smooth or uneven, and at the same time, the authors classified cases where US failed to diagnose placenta accreta as Grade 0.…”

Section: Methodsmentioning

confidence: 99%

Application of Ultrasound Combined with Magnetic Resonance Imaging in the Diagnosis and Grading of Patients with Prenatal Placenta Accreta

2022

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In order to study the clinical application value of placenta accreta (PIA) diagnosis and grading, the authors propose a method based on ultrasound combined with magnetic resonance imaging in the diagnosis and grading of prenatal placenta accreta patients. This method is adopted in materials and methods: a retrospective analysis of hospital patients with high suspicion of placenta accreta by clinical or ultrasonography between October 2019 and October 2021, the imaging and clinical data of 312 patients who underwent placental MRI examination. The MRI imaging data of all patients were jointly analyzed, and the main observation indicators are as follows: (1) dark zone in the placenta, (2) disruption of the border of the myometrium, (3) disruption of the myometrium, (4) abnormal blood vessels in the placenta, (5) enlargement of the lower part of the uterus, and (6) local bulge of the bladder/or invasion of the adjacent tissues of the uterus. The results show the following: in MRI combined with ultrasonography ( P < 0.05 ), there was no statistical significance in the specificity and accuracy of MRI combined with ultrasound to diagnose PIA ( P > 0.05 ). The comparison of graded diagnostic accuracy showed that in ultrasound alone < MRI alone < MRI combined with ultrasound, the differences were statistically significant ( P < 0.05 ). Ultrasound combined with MRI in the diagnosis of placenta accreta is in good agreement with the clinical and surgical pathological results; MRI examination can be used as an important method for prenatal placenta accreta screening. MRI can classify placenta accreta to some extent.

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Section: Methodsmentioning

confidence: 99%

Application of Ultrasound Combined with Magnetic Resonance Imaging in the Diagnosis and Grading of Patients with Prenatal Placenta Accreta

2022

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“…AI algorithms such as deep learning (DL) can efficiently and automatically analyze medical images, with outstanding capabilities in locating lesions and extracting characteristic features from medical images ( 15 ). DL has been introduced to assist clinicians in breast lesion identification and segmentation, cancer grading while allowing reproducibility and visualization ( 16 – 18 ). AI has been applied for ultrasound assessment of NAC treatment response in breast cancer ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

Zhang,

Deng,

Huang

et al. 2024

Front. Oncol.

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PurposeThe aim of this study was to investigate the value of a deep learning model (DLM) based on breast tumor ultrasound image segmentation in predicting pathological response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodsThe dataset contains a total of 1393 ultrasound images of 913 patients from Renmin Hospital of Wuhan University, of which 956 ultrasound images of 856 patients were used as the training set, and 437 ultrasound images of 57 patients underwent NAC were used as the test set. A U-Net-based end-to-end DLM was developed for automatically tumor segmentation and area calculation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional ultrasound measurement methods (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using changes in tumor size ratios to develop receiver operating characteristic curves.ResultsThe average intersection over union value of the DLM was 0.856. The early-stage ultrasound-predicted area under curve (AUC) values of pCR were not significantly different from those of the intermediate and late stages (p< 0.05). The AUCs for MSM, DLM, LAM and DAM were 0.840, 0.756, 0.778 and 0.796, respectively. There was no significant difference in AUC values of the predictive ability of the four models.ConclusionUltrasonography was predictive of pCR in the early stages of NAC. DLM have a similar predictive value to conventional ultrasound for pCR, with an add benefit in effectively improving workflow.

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“…AI algorithms such as deep learning (DL) can e ciently and automatically analyze medical images, with outstanding capabilities in locating lesions and extracting characteristic imaging features from medical images [17]. In breast ultrasound imaging, DL has been introduced to assist physicians in lesion identi cation and segmentation, feature extraction, and discrimination of benign and malignant lesions while allowing reproducibility and visualization [18][19][20]. AI has been applied for ultrasound assessment of NAC treatment response in breast cancer [21].…”

Section: Introductionmentioning

confidence: 99%

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

Zhang

Deng

Huang

et al. 2023

Preprint

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Purpose: The present study investigated whether deep learning models (DLMs) could replace traditional ultrasound measurement models for predicting pathological responses to neoadjuvant chemotherapy (NAC) for breast cancer. Methods: Data from 57 patients (443 ultrasound images) who underwent NAC followed by surgery were analyzed. A DLM was developed for accurate breast tumor ultrasound image segmentation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional measurement models (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using tumor size ratios and receiver operating characteristic curves. Results: The average intersection over the union value of the DLM was 0.8087. MSM showed the best performance with an area under the curve (AUC) of 0.840; DLM performance was slightly weaker with an AUC of 0.756. The AUCs of the two traditional models were 0.778 for LAM and 0.796 for DAM. There was no significant difference in AUC values of the predictive ability of the four models. Moreover, no significant difference in AUC values of ultrasound prediction was noted between each NAC cycle (p<0.05). Conclusion: Patients in the pCR group had a significantly better response than those in the non-pCR group, and ultrasonography was predictive of pCR in the early stages of NAC. DLMs can replace traditional measurements for predicting pCR.

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Surface Point Cloud Ultrasound with Transcranial Doppler: Coregistration of Surface Point Cloud Ultrasound with Magnetic Resonance Angiography for Improved Reproducibility, Visualization, and Navigation in Transcranial Doppler Ultrasound

Cited by 4 publications

References 31 publications

Application of Ultrasound Combined with Magnetic Resonance Imaging in the Diagnosis and Grading of Patients with Prenatal Placenta Accreta

Application of Ultrasound Combined with Magnetic Resonance Imaging in the Diagnosis and Grading of Patients with Prenatal Placenta Accreta

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

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