From 83892985199e65b3feb5dd149f1e8b8ba5d613d0 Mon Sep 17 00:00:00 2001 From: Johannes Hofmanninger Date: Mon, 4 May 2020 12:22:07 +0200 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index baef811..051e7da 100644 --- a/README.md +++ b/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.