Purpose　Accurate　recognition　of　medullary　thyroid　carcinoma　(MTC)　is　of　great　importance　in　medical　diagnosis,　as　MTC　is　rare　but　second-most　malignant　thyroid　cancers　with　a　high　case-fatality　ratio.(1)　But　there　is　a　lower　recognition　rate　on　distinguishing　MTC　from　other　thyroid　nodules　in　ultrasound　images,　even　by　experienced　experts.　This　paper　introduces　the　computer-aided　method　to　tackle　the　challenge　of　recognizing　MTC　from　ultrasound　images,　including　limited　MTC　samples,　and　ambiguities　among　MTC,　benign　nodules,　and　papillary　thyroid　carcinoma　(PTC).　Methods　The　recognition　of　MTC　based　on　large　MTC　samples　of　ultrasound　images　has　never　been　explored,　as　only　one　existing　work　presented　a　relevant　dataset　with　a　limited　21　MTC　samples.　This　study　proposes　a　novel　method　for　primarily　differentiating　MTC　samples　from　benign　nodules　and　PTC　that　is　the　most　common　thyroid　cancer.　Our　method　is　a　two-stage　schema　with　two　important　components　including　a　cascaded　coarse-to-fine　segmentation　network　and　a　knowledge-based　classification　network.　The　cascaded　coarse-to-fine　segmentation　network　incorporates　two　U-Net++　networks　for　improving　the　segmentation　results　of　thyroid　nodules.　Meanwhile,　our　knowledge-based　classification　network　extracts　and　fuses　semantic　features　of　solid　tissues　and　calcification　for　better　recognizing　the　segmented　nodules　from　the　ultrasound　images.　In　our　experiments,　dice　similarity　coefficient　(DSC),　intersection　over　union　(IoU),　precision,　recall,　and　Hausdorff　distance　(HD)　are　adopted　for　evaluating　the　segmentation　results　of　thyroid　nodules,　and　accuracy,　precision,　recall,　and　F1-score　are　used　for　classification　evaluation.　Results　We　present　a　well-annotated　dataset　including　samples　of　248　MTC,　240　benign　nodules,　and　239　PTC.　For　thyroid　nodule　segmentation,　our　designed　cascaded　segmentation　network　attains　values　of　0.776　DSC,　0.689　IoU,　0.778　precision,　and　0.821　recall,　respectively.　By　incorporating　prior　knowledge,　our　method　achieves　a　mean　accuracy　of　82.1%　in　classifying　thyroid　nodules　of　MTC,　PTC,　and　benign　ones.　Especially,　our　method　gains　the　higher　performance　in　recognizing　MTC　with　an　accuracy　of　86.8%,　compared　to　nearly　70%　diagnosis　accuracy　of　experienced　doctors.　The　experimental　results　on　our　Fujian　Provincial　Hospital　dataset　further　validate　the　efficiency　of　our　proposed　method.　Conclusions　Our　proposed　two-stage　method　incorporates　pipelines　of　thyroid　nodules　segmentation　and　classification　of　MTC,　individually.　Quantitative　and　qualitative　results　indicate　that　our　proposed　model　achieves　accurate　segmentation　of　thyroid　nodules.　The　results　also　validate　that　our　learning-based　framework　facilitates　the　recognition　of　MTC,　which　gains　better　classification　accuracy　than　experienced　doctors.