LiTS – Liver Tumor Segmentation Challenge (LiTS17)
Patrick Christ

LITS17 (262 files) 61.06kB 58.74kB 257.34kB 279.49kB 682.10kB 225.11kB 249.21kB 274.87kB 262.23kB 247.34kB 257.11kB 262.97kB 277.80kB 404.15kB 278.67kB 276.68kB 533.81kB 377.53kB 311.28kB 301.80kB 300.34kB 236.82kB 85.54kB 202.73kB 159.62kB 284.89kB 280.87kB 387.45kB 149.35kB 116.40kB 146.88kB 77.99kB 126.39kB 134.47kB 108.55kB 152.18kB 91.53kB 109.27kB 103.52kB 211.22kB 124.83kB 100.63kB 92.85kB 149.29kB 128.60kB 71.04kB 64.69kB 115.60kB 102.68kB 100.06kB 94.80kB 94.19kB 95.40kB 50.62kB 50.13kB 180.16kB 196.13kB 216.12kB 112.28kB 113.84kB 149.55kB 117.47kB 99.11kB 86.31kB 136.16kB 368.53kB 71.05kB 138.51kB 609.21kB 592.14kB 262.50kB 244.01kB 257.08kB 286.83kB 258.35kB 215.07kB 146.90kB 225.94kB 461.07kB 349.24kB 523.29kB 813.55kB 1.30MB 340.26kB 444.33kB 416.30kB 378.98kB 391.01kB 349.59kB 350.58kB 356.40kB 305.99kB 350.82kB 397.27kB 385.71kB 359.47kB 418.74kB 439.26kB 363.45kB 314.10kB 455.16kB 624.01kB 389.19kB 288.69kB 338.11kB 404.90kB 290.28kB 332.53kB 547.60kB 335.38kB 348.41kB 385.26kB 350.04kB 359.38kB 369.53kB 352.21kB 416.22kB 493.93kB 216.94kB 208.74kB 186.47kB 199.77kB 193.18kB 227.36kB 170.01kB 186.44kB 200.36kB 417.81kB 411.47kB 467.54kB 382.46kB 17.55MB 29.69MB 160.08MB 170.20MB 216.36MB 137.63MB 147.38MB 170.35MB 158.36MB 161.82MB 154.85MB 148.34MB 145.86MB 169.29MB 182.01MB 174.19MB 171.46MB 212.58MB 205.53MB 173.91MB 173.68MB 130.21MB 57.41MB 115.59MB 85.49MB 166.60MB 176.75MB 247.76MB 32.21MB 38.78MB 52.50MB 25.59MB 36.33MB 35.65MB 34.13MB 31.64MB 30.30MB 32.27MB 33.34MB 62.31MB 29.86MB 28.34MB 28.39MB 39.74MB 28.90MB 19.29MB 29.20MB 55.11MB 57.42MB 60.02MB 55.59MB 58.89MB 56.04MB 26.07MB 26.98MB 56.31MB 67.24MB 111.28MB 62.12MB 65.83MB 73.52MB 53.37MB 53.75MB 31.04MB 65.78MB 130.21MB 26.67MB 46.66MB
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Type: Dataset

title= {LiTS – Liver Tumor Segmentation Challenge (LiTS17)},
keywords= {},
author= {Patrick Christ},
abstract= {The liver is a common site of primary (i.e. originating in the liver like hepatocellular carcinoma, HCC) or secondary (i.e. spreading to the liver like colorectal cancer) tumor development. Due to their heterogeneous and diffusive shape, automatic segmentation of tumor lesions is very challenging. Until now, only interactive methods achieved acceptable results segmenting liver lesions.

With our challenge we encourage researchers to develop automatic segmentation algorithms to segment liver lesions in contrast­-enhanced abdominal CT scans. The data and segmentations are provided by various clinical sites around the world. The training data set contains 130 CT scans and the test data set 70 CT scans. The challenge is organised in conjunction with ISBI 2017 and MICCAI 2017. For MICCAI 2017 we added tasks for liver segmentation and tumor burden estimation.



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terms= {},
license= {},
superseded= {},
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10 day statistics (43 downloads)

Average Time 6 mins, 35 secs
Average Speed 42.08MB/s
Best Time 5 mins, 00 secs
Best Speed 55.52MB/s
Worst Time 33 mins, 43 secs
Worst Speed 8.23MB/s