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Pelvis

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Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

Skeletal radiology
PURPOSE: To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Radiation oncology (London, England)
BACKGROUND: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvi...

Robot-assisted donor hysterectomy in uterus transplantation - a modality to increase reproducibility.

Ginekologia polska
Uterus transplantation is a non-lifesaving vascularized composite allotransplantation procedure requiring immunosuppression until removal of the graft. The focus of uterus transplantation is changing regarding refining individual treatment procedures...

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

Medical physics
PURPOSE: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-bas...

Extraperitoneal Laparoscopic Versus Transperitoneal Robot-Assisted Laparoscopic Approaches for Extended Pelvic Lymph Node Dissection During Radical Prostatectomy.

Journal of laparoendoscopic & advanced surgical techniques. Part A
We aim to directly compare the feasibility and safety of extended pelvic lymph node dissection (PLND) during transperitoneal robotic-assisted radical prostatectomy (Tp-RARP) and extraperitoneal laparoscopic radical prostatectomy (Ep-LRP). We retros...

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.

International journal of radiation oncology, biology, physics
PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.

Deep learning to segment pelvic bones: large-scale CT datasets and baseline models.

International journal of computer assisted radiology and surgery
PURPOSE: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited ...

Model construction and application for automated measurement of CE angle on pelvis orthograph based on MASK-R-CNN algorithm.

Biomedical physics & engineering express
Developmental dysplasia of the hip (DDH) is a common orthopedic disease. A simple and cost-effective scientific tool for assisting the early diagnosis of DDH is urgently needed. This study proposed a new artificial intelligence (AI) model for automat...

Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Reports of machine learning implementations in veterinary imaging are infrequent but changes in machine learning architecture and access to increased computing power will likely prompt increased interest. This diagnostic accuracy study describes a pa...

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.

Nature communications
Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related ...