Unfortunately, it does not support DICOM or NIfTI formats.

It allows the processing of the following formats: PNG, GIF, TIFF, JPEG, BMP. Sefexa is a segmentation software for 2D images. Some recent scientific articles using RIL-contour for segmentation. Pros : easy to install, video tutorials to get started, user friendly segmentation tools, the user can load segmentation algorithms for automatic processing, use of the main medical data formats (DICOM, NIfTI).Ĭons : the tool is very extensive, the user needs to take the time to go through the menus to unlock certain features or functionalities. We were unable to test this functionality because we did not have segmentation models available.Īnnotations can be exported in NIfTI format. The user can load its own models and then apply them to the dataset. The software also offers the use of machine learning models for segmentation or classification. It offers, in addition, annotation tools such as tags and measures: dot, angle, arrow, circle, free form, text, distance measurement, gap measurement.Īnnotations can be copied from one image to another in the series. We have already used this software for anonymizing DICOM files. Which free software to segment medical images?

does not support DICOM or NIfTI medical data formats,.

operated via virtual machines, or directly integrated with medical image collection and storage tools,.

Others were excluded (ePad, XANAT , GeoS, MammoApplet, etc.), for one or more of the following reasons: For our project, we have selected a few tools proposed by, presented below. During the review, we searched for a free DICOM image annotation software to improve our dataset with the corresponding ROI annotations. article Medical image annotations for deep learning), we were able to begin the data preparation phase. After defining the annotation formats required for this type of task (cf. Our current medical image analysis project uses deep learning object detection algorithms.

allows the user to simply export these annotations in a format compatible with the data processing pipeline of a deep learning algorithm.

provides all the necessary tools for manual detection by the radiologist, of the region of interest (ROI), and.

takes into account DICOM files, to simplify the processing of data from the care facility’s PACS (Picture Archiving and Communication System),.

To facilitate this step, and to minimize the annotation time, the user will need an annotation tool which: This annotation is very time-consuming and requires the expertise of radiologists. Deep learning algorithms for the detection of objects (abnormality, anatomical structure) in medical imaging require datasets annotated with precision to achieve acceptable results for medical use.

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