Colorectal　cancer　is　one　of　the　digestive　tract　malignant　tumors　in　life.　In　recent　years,　the　incidence　rate　of　rectal　cancer　has　increased　significantly　in　China.　The　segmentation　of　CT　images　of　colorectal　tumors　is　a　tedious,　time-consuming　and　expensive　job.When　diagnosing　rectal　cancer,　it　would　be　helpful　to　accurately　segment　the　rectal　tumor　region　from　CT　images　so　that　doctors　could　make　more　accurate　diagnoses　and　thus　have　better　results　for　the　disease.　Based　on　the　literature　review,　this　paper　presents　medical　image　segmentation　algorithms,　including　traditional　algorithms　based　on　energy　minimization,　and　evaluates　them　according　to　the　metrics　used.　The　paper　also　describes　the　public　datasets　available　for　use　in　the　study.　In　this　paper,　various　segmentation　methods　are　analyzed　in　detail.　Their　theoretical　and　practical　characteristics　are　described　for　each　group　of　methods,　and　the　most　advanced　and　promising　methods　are　proposed.　This　paper　can　help　understand　the　available　colorectal　segmentation　methods,　making　developing　new　and　more　effective　and　improving　existing　methods　easier.　The　method　is　simple　to　interact　with,　robust　and　accurate,　and　can　effectively　assist　physicians　in　achieving　diagnosis　and　surgical　planning.　©　2022　SPIE.