Update README.md

README.md

@@ -4,7 +4,7 @@ This package provides trained U-net models for lung segmentation. For now, four
 
 - U-net(R231): This model was trained on a large and diverse dataset that covers a wide range of visual variabiliy. The model performs segmentation on individual slices, extracts right-left lung seperately includes airpockets, tumors and effusions. The trachea will not be included in the lung segmentation. https://arxiv.org/abs/2001.11767
 
-- U-net(LTRCLobes): This model was trained on a subset of the [LTRC](https://ltrcpublic.com) dataset. The model performs segmentation of individual lung-lobes but yields limited performance when dense pathologies are present. 
+- U-net(LTRCLobes): This model was trained on a subset of the [LTRC](https://ltrcpublic.com) dataset. The model performs segmentation of individual lung-lobes but yields limited performance when dense pathologies are present or when fissures are not visible at every slice. 
 
 - U-net(LTRCLobes_R231): This will run the R231 and LTRCLobes model and fuse the results. False negatives from LTRCLobes will be filled by R231 predictions and mapped to a neighbor label. False positives from LTRCLobes will be removed. The fusing process is computationally intensive and can, depdending on the data and results, take up to several minutes per volume.