Ultrasonic　segmentation　of　thyroid　nodules　is　an　indispensable　part　of　computer　aided　system　and　diagnosis　of　thyroid　diseases.　Due　to　the　fact　that　ultrasound　images　have　the　characteristics　of　asymmetric　irradiation,　uneven　appearance,　low　signal-to-noise　ratio,　low　contrast　and　blurred　boundary,　thyroid　nodule　segmentation　is　a　challenging　task.　In　order　to　improve　the　precision　of　thyroid　nodule　segmentation,　this　paper　presents　an　Ultrasound　thyroid　nodule　segmentation　algorithm　based　on　multi-branch　and　color　space　volume.　In　this　method,　an　image　pyramid　is　constructed　to　compensate　for　the　spatial　information　loss　by　convolutional　and　pooling　operations.　We　use　pretrained　ResNet　block　as　the　Feature　Encoder　Module,　a　multi-branch　convolutional　fusion　(MBCF)　block　is　proposed　to　increase　the　receptive　field　and　capture　high-level　spatial　and　semantic　information.　To　further　improve　the　segmentation　performance,　the　color　space　volume　transformation　is　used　to　highlight　the　contrast　between　the　target　area　and　the　background　in　the　data　preprocessing　part.　Experimental　results　on　an　Ultrasound　thyroid　nodule　segmentation　dataset　show　that　the　proposed　method　performs　well　over　the　state-of-the-art　methods.　©　2021　IEEE.