Objective　To　explore　the　performance　of　a　deep　learning　algorithm　that　combined　multi‑view　fusion　with　active　contour　constrained　for　ossicles　segmentation　on　the　10　μm　otology　CT　images.　Methods　The　10　μm　otology　CT　image　data　from　79　cases　(56　cases　were　from　volunteers　and　23　cases　were　from　specimens)　were　retrospectively　collected　in　the　Radiology　Department　of　Beijing　Friendship　Hospital　from　October　2019　to　December　2020.　An　annotation　of　malleus,　incus,　and　stapes　were　conducted.　Then　the　datasets　were　established　and　were　divided　into　training　set　(n=55),　validation　set　(n=8),　and　test　set　(n=16).　Using　the　rapid　localization　of　the　region　of　interest　combined　with　the　precise　segmentation　algorithm,　the　malleus,　incus　and　stapes　were　segmented　and　fused　from　three　perspectives　of　coronal,　sagittal　and　cross‑sectional　views.　Besides,　an　active　contour　loss　was　designed　simultaneously　for　the　segmentation　of　stapes.　Dice　similarity　coefficient　(DSC)　was　used　as　the　objective　evaluation　metric　for　the　evaluation　of　the　segmentation　results.　The　inter　group　DSC　of　the　proposed　method　was　compared　with　that　of　the　basic　method　and　other　methods.　Results　The　average　DSC　values　of　the　multi‑view　fusion　segmentation　algorithm　for　malleus,　incus　and　stapes　reached　up　to　94.2%±2.7%,　94.6%±2.6%　and　76.0%±5.5%,　respectively.　After　adopting　the　constraint　of　active　contour　loss　method,　the　average　DSC　of　stapes　was　improved　(76.4%±5.4%　vs　76.0%±5.5%).　The　visualization　results　also　demonstrated　that　the　segmentation　results　of　the　stapes　were　more　complete.　Conclusions　Multi‑view　fusion　algorithm　based　on　10　μm　otology　CT　images　can　realize　accurate　segmentation　of　malleus　and　incus.　Combined　with　the　constraint　of　active　contour　loss　method,　the　segmentation　accuracy　of　stapes　can　be　further　improved.　©　2021　Chinese　Medical　Association.　All　rights　reserved.