Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound

Xia, Wenyao; Ameri, Golafsoun; Fakim, Djalal; Akhuanzada, Humayon; Raza, Malik Z.; Shobeiri, S. Abbas; McLean, Linda; Chen, Elvis C. S.

doi:10.1109/tmi.2022.3199968

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…We further increase the network model’s capabilities for segmentation by using a CNN-transformer hybrid model. This approach is similar to the approaches outlined in Feng ( 2021 ), Liu et al ( 2020 ), Frésard ( 2020 ), Meneghetti et al ( 2020 ) and Xia ( 2022 ). Recent years have seen a rise in the use of CNN-transformer hybrid models for the segmentation of multimodal brain tumors as well as other 2D and 3D medical images (Vukicevic 2020 ; Zhang et al 2021 ).…”

Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 77%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 77%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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show abstract

“…While Williams et al 21 did not report a DSC for their work using a U-Net model and 73 test images. Most recently, Xia et al 22 developed an automated method to both extract the PMHD and segment the levator hiatus. They achieved DSC of 89% and 88% for patients performing rest (73 test images) and contraction (35 test images) respectively but did not include images of the Valsalva maneuver.…”

Section: Discussionmentioning

confidence: 99%

Automated segmentation and measurement of the levator hiatus in 3D transperineal ultrasound

Szentimrey

Ameri²,

Cheung

et al. 2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Three-dimensional (3D) transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Patients with POP have abnormal descent of one or more pelvic organs (i.e., bladder, uterus, vagina) through the levator hiatus, which is often experienced by the patient as a persistent bothersome bulge protruding from the vaginal opening. The enlargement of the hiatal opening measured in the plane of minimal hiatal dimensions (PMHD), has been used as an indication for POP severity. Manually measuring the size of the levator hiatus in 3D TPUS images can be challenging and requires expertise and training and is timeconsuming. Hence a fully automated method for estimating the dimensions of hiatal opening is highly desirable. To this end, we developed a fully automated method to segment the levator hiatus from the PMHD based on the nnU-Net model framework. We trained, validated, and tested on a total of 252 3D US images from 138 patients that may have POP as determined by the pelvic organ prolapse quantification (POP-Q) system. As a benchmark comparison, we compared the nnU-Net to a vanilla U-Net whose hyperparameters were manually tuned. Model performance was determined using Dice similarity coefficient (DSC) and levator hiatus width, length, and area by comparing the model segmentations to manual segmentations. The nnU-Net achieved a DSC of 93.1%±3.3%, absolute width difference of 2.3mm±1.7mm, absolute length difference of 2.6mm±2.5mm and absolute area difference of 1.8cm2±1.3cm2.

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“…The results are similar to those manually segmented by two experts and faster than manual segmentation by 2 minutes. In 2022, Xia [15] et al proposed an automatic identification and segmentation model that not only identifies and segments defects in the resting state but also in the contracted state. Its accuracy is relatively high, with an average Dice of 0.89 and an average boundary distance of 2.25 mm in the resting state and an average Dice of 0.88 and an average boundary distance of 2.75 mm in the contracted state.…”

Section: Fully Automatic Segmentation Model Based On Reconstructed Le...mentioning

confidence: 99%

Advanced Application of Artificial Intelligence for Pelvic Floor Ultrasound in Diagnosis and Treatment

Qu¹,

Zhang²

2023

Advanced Ultrasound in Diagnosis and Therapy

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Artificial intelligence-based pelvic floor ultrasound helps the diagnosis, preoperative assessment, and postoperative monitoring of female pelvic floor dysfunction (FPFD). The application of artificial intelligence in pelvic floor ultrasound mainly includes automatic segmentation and measurement, the diagnosis of muscle injury, childbirth prediction and postoperational evaluation. It can not only overcome the problem of operator experience dependence but also improve work efficiency and simplify the workflow, which has popularized the application of pelvic floor ultrasound. However, most of the current research is still limited to the automatic segmentation of three-dimensional axial plane levator hiatus (LH). The automatic reconstruction, real-time tracking of 3D/4D images and the imaging navigation of pelvic floor surgery remain major challenges for researchers.

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Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound

Cited by 8 publications

References 37 publications

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Automated segmentation and measurement of the levator hiatus in 3D transperineal ultrasound

Advanced Application of Artificial Intelligence for Pelvic Floor Ultrasound in Diagnosis and Treatment

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