However, this strategy may be of limited reliability when comparing dramatically different images collected from different modalities. Other registration strategies bypass modality-specific segmentation or points detection steps: (i) Iconic methods involve direct image comparisons computing pixel-based similarity metrics ( Beg et al., 2005 Vercauteren et al., 2009) (ii) Hybrid methods combine Iconic and segmentation-based strategies ( Commowick et al., 2006 Ourselin et al., 2000). While these two families of methods gave rise to several 3D registration tools, spanning from intermodal registration in human medicine ( Kikinis et al., 2014 Klein et al., 2010 Vichot et al., 2012 Yoo et al., 2002) to time-lapse tracking in plant imaging ( Fernandez et al., 2010 Michelin et al., 2016), no comprehensive and user-friendly solution is yet available to achieve both goals. In ImageJ/Fiji ( Schindelin et al., 2012), existing plugins imply either human assistance for 3D registration (Atlas toolkit, Grocott et al., 2016), coding skills to achieve analysis of image series (Elastix, Klein et al., 2010), or are limited to handling a single imaging modality. Fijiyama addresses this concern, deriving biomedical algorithmic developments to build a generic registration plugin for Fiji, enabling multimodal and time-lapse 3D image registration by non-specialists. Two pairwise image registration: Registration means estimating the geometrical transformation of a moving image to achieve the best possible superimposition with a reference image. MEDINRIA VIDE MANUALįijiyama proceeds with refining steps including an optional manual alignment in a tridimensional space for large deformations ( Fig. 1a), and automatic registration steps estimating rigid transformations, similarities or deformation fields ( Supplementary Data). Two optimization strategies can be applied: Block Matching or Iconic registration. After registration, superimposition can be investigated visually, or quantitatively, using the mismatch measurement tool. Validated transformations (matrices and/or deformation fields) and transformed images can be saved and reused either in the plugin or in Fiji. The interest clinicians have shown in MedINRIA so far indicates that the need of such simple, yet powerful softwares is real and increasing.Registration of ( a) MRI images from a grapevine cutting: structures present at day 0 (red), day 35 (green) and at both dates (yellow) ( b) X-ray (red) and MRI (green) images from a human abdomen ( c) photographs (left), X-rays (center) and MRI (right) images from a grapevine trunk, in transversal (up) and longitudinal (down) views ( d) 3D time series from a living grapevine cutting acquired by MRI, using a mask to prevent local tissue deformation from being corrected ( Supplementary Data). Other MedINRIA tools are underway to make cutting edge research in medical imaging rapidly available to clinicians. Med-INRIA is a free software, available on Windows, Linux and MacOSX. Then, we focus on the features that make interactions with data even more intuitive. For each of these tools, we first introduce its dedicated application and the processing methods it contains. It provides state-of-the-art algorithms while keeping a user-friendly graph-ical interface. The MedINRIA software was born to fill this lack and consists of a collection of tools that optimally exploit various types of data (e.g., 3D images, diffusion tensor fields, neural fibers as obtained in DT-MRI). Moreover, they need new solutions to fully exploit their data, but they often cannot access state-of-the-art methods as those are mostly available in complicated softwares. Indeed, clinicians need ergonomic, efficient, intuitive and reactive softwares. However, it remains challenging because the diversification and complexification of the available sources of information, as well as the specific requirements of clinicians, make it difficult to solve in a computer science point of view. Processing and visualization of 3D medical data is nowadays a common problem.
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