Right　ventricular　(RV)　segmentation　from　magnetic　resonance　imaging　(MRI)　is　a　crucial　step　for　cardiac　morphology　and　function　analysis.　However,　automatic　RV　segmentation　from　MRI　is　still　challenging,　mainly　due　to　the　heterogeneous　intensity,　the　complex　variable　shapes,　and　the　unclear　RV　boundary.　Moreover,　current　methods　for　the　RV　segmentation　tend　to　suffer　from　performance　degradation　at　the　basal　and　apical　slices　of　MRI.　In　this　work,　we　propose　an　automatic　RV　segmentation　framework,　where　the　information　from　long-axis　(LA)　views　is　utilized　to　assist　the　segmentation　of　short-axis　(SA)　views　via　information　transition.　Specifically,　we　employed　the　transformed　segmentation　from　LA　views　as　a　prior　information,　to　extract　the　ROI　from　SA　views　for　better　segmentation.　The　information　transition　aims　to　remove　the　surrounding　ambiguous　regions　in　the　SA　views.　We　tested　our　model　on　a　public　dataset　with　360　multi-center,　multi-vendor　and　multi-disease　subjects　that　consist　of　both　LA　and　SA　MRIs.　Our　experimental　results　show　that　including　LA　views　can　be　effective　to　improve　the　accuracy　of　the　SA　segmentation.　Our　model　is　publicly　available　at　https://github.com/NanYoMy/MMs-2.　©　2022,　Springer　Nature　Switzerland　AG.