Baby Segmentation Tool is a software application used to segment placental and fetal anatomy from MRI volumes. The software provides fully automatic segmentation using artificial intelligence, as well as manual delineation and image navigation.
Baby Segmentation Tool offers many supporting utilities. Some of the core advantages of include:
Baby Segmentation Tool will automatically detect different DICOM series and convert them to the correct NIFTI files.
Baby Segmentation Tool supports visualisation and navigation in both 2D and 3D. Including that, does Baby Segmentation Tool volume sampling and opacity control.
Baby Segmentation Tool uses AI to do sematic segmentation with the accuracy of a professional trained surgeon, but uses just a fraction of the time.
Baby Segmentation Tool supports manual annotation to add/subtract structures, or create a additional structures to indicate abnormalities.
Baby Segmentation Tool can collect statistics as voxel counts and cubic millimeters in the different structures of the 3D volume.
Baby Segmentation Tool supports 3D model exports to industri standard foramts. Making it possible to visualize the models in VR/AR or 3D print them.
Baby Segmentation Tool makes it easy from start to end. With only a few steps along the way, can you visualize your MRI images in 3D in a few minutes.
For best results, use the suggested protocols provided under resources. Choose a protocol consisting of a bFFE / FIESTA / True FISP sequence. All volumes scanned must share the same geometry.
Convert your MRI DICOM images into the Nifti format using this feature. Watch and learn from our tutorials.
Select the input nifti file directory and choose the desired window resolution. Keep in mind that higher resolutions demand more available computer resources. Please be patient as the software is inferring.
You may now view, annotate and export your results.
This study presents the development of an AI deep learning algorithm designed to accurately estimate placental and fetal volumes from MR scans. Utilizing 193 MR scans from normal pregnancies, the research employed the DenseVNet neural network for segmentation, compared against manual annotations. The study achieved a high Dice Score Coefficient, indicating reliable accuracy of the AI algorithm. Notably, the neural network significantly reduced volume estimation time from over an hour to less than 10 seconds, demonstrating both the precision and efficiency of AI in medical imaging.
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Recognized for exceptional advancements in MRI segmentation tools, enhancing accuracy and efficiency.