diff --git a/msd-rankings-subtasks-paper.R b/msd-rankings-subtasks-paper.R
index 2285f88..7f24b3a 100644
--- a/msd-rankings-subtasks-paper.R
+++ b/msd-rankings-subtasks-paper.R
@@ -1,120 +1,148 @@
 library(rstudioapi)
 current_path = rstudioapi::getActiveDocumentContext()$path
 setwd(dirname(current_path))
 
 library(challengeR)
 library(doParallel)
 library(plyr)
 source("msd-prepare-data.R")
 
 ###########################
 # Define functions
 ###########################
 
 computeSubtaskResults <- function(data) {
   # Create challenge object
   challenge= data %>% 
-    as.challenge(value = "value", algorithm = "alg_name",case = "case_task",by = "subtask", smallBetter = F, na.treat = 0) 
+    as.challenge(value = "value", algorithm = "alg_name",case = "case_task", by = "subtask", smallBetter = F, na.treat = 0) 
   
   # Compute significance ranking
   sigRanking=challenge %>% testThenRank(ties.method = "min",alpha=.05)
   
   # Compute consensus ranking over all subtasks (regions)
   consensusRanks <- sigRanking%>%consensus(method = "euclidean") 
   
   # Get subtask rankings
   algorithms <- names(consensusRanks)
   subtasks <- unique(data$subtask)
   
   rankings <- getSubtaskRankings(sigRanking, subtasks, algorithms)
   
   return(rankings)
 }
 
 
 getSubtaskRankings <- function(data, subtasks, sorting) {
   # Create data frame for rankings
   rankings <- data.frame(algorithm = sorting)
   colnames(rankings) <- c("Algorithm")
 
   for(subtask in subtasks) {
     # Select subtask rankings from ranking lists
 
     subtaskRanking <- tibble::rownames_to_column(data$matlist[subtask][[1]], "Algorithm")
     sigRanking <- subtaskRanking[match(sorting, subtaskRanking$Algorithm),]
     
     new_colnames <- c(colnames(rankings), paste("rank",subtask, sep="_"))
     
     # Write all rankings in one data frame
     rankings <- cbind(rankings, sigRanking$rank)
     colnames(rankings) <- new_colnames
   }
 
   return(rankings)
 }
 
+getAggregatedRanking <- function(data, rankings) {
+  # Initialize data frame for all organ average ranks
+  rankingsAllOrgans <- data.frame(Algorithm = rankings$Algorithm)
+  
+  tasks <- unique(data$task)
+  
+  # Iterate over all tasks
+  for(t in tasks) {
+    # Get corresponding subtasks
+    subtasks <- unique(subset(data, task == t)$subtask)
+    
+    rankingsOrgan <- data.frame(matrix(vector(), 19, 0))
+    # Iterate over subtasks
+    for(s in subtasks) {
+      rankingsOrgan <- cbind(rankingsOrgan, rankings[, grepl(s, names(rankings))])
+    }
+    
+    # Average ranks
+    rankingsOrgan$meanRank <- rowMeans(rankingsOrgan)
+    
+    # Rank over averages
+    rankingsOrgan$rank <- rank(rankingsOrgan$meanRank) 
+    
+    # Add to data frame
+    rankingsAllOrgans[,ncol(rankingsAllOrgans)+1] <- rankingsOrgan$rank
+    colnames(rankingsAllOrgans)[ncol(rankingsAllOrgans)] <- paste("rank", t, sep="_")
+  }
+  
+  rankingsAllOrgans$MeanRank <- rowMeans(rankingsAllOrgans[,2:ncol(rankingsAllOrgans)])
+  rankingsAllOrgans$rank <- rank(rankingsAllOrgans$MeanRank)
+  
+  return(rankingsAllOrgans)
+}
+
 ###########################
 # Read and prepare challenge data
 ###########################
 
 msd_data_matrix <- prepare_data()
 
 # Prepare data subsets for each metric and phase
 dataDSC= msd_data_matrix %>% subset(score=="DSC")
 dataNSD= msd_data_matrix %>% subset(score=="NSD")
 dataDSCph1= msd_data_matrix %>% subset(score=="DSC" & phase==1)
 dataNSDph1= msd_data_matrix %>% subset(score=="NSD" & phase==1)
 dataDSCph2= msd_data_matrix %>% subset(score=="DSC" & phase==2)
 dataNSDph2= msd_data_matrix %>% subset(score=="NSD" & phase==2)
 
 ###########################
 # Compute Phase 1 Results
 ###########################
 
 ## DSC metric results
 rankingsPh1_dsc <- computeSubtaskResults(dataDSCph1)
 write.csv(rankingsPh1_dsc, "rankings-per-subtask/subtask-rankings-ph1-dsc.csv")
 
 ## NSD metric results
 rankingsPh1_nsd <- computeSubtaskResults(dataNSDph1)
 write.csv(rankingsPh1_nsd, "rankings-per-subtask/subtask-rankings-ph1-nsd.csv")
 
-## Aggregate rankings by averaging over all subtasks
-rankingsMeanPh1 <- cbind(rankingsPh1_dsc, rankingsPh1_nsd[,2:14])
+# Merge DSC and NSD rankings in one data frame and rename columns
+rankingsPh1 <- cbind(rankingsPh1_dsc, rankingsPh1_nsd[,2:14])
+colnames(rankingsPh1)[2:14] <- paste(colnames(rankingsPh1)[2:14], "dsc", sep="_")
+colnames(rankingsPh1)[15:27] <- paste(colnames(rankingsPh1)[15:27], "nsd", sep="_")
 
-# Calculate average ranks
-rankingsMeanPh1 <- cbind(rankingsMeanPh1,rowMeans(rankingsMeanPh1[,2:27]))
+# Get aggregated ranking
+aggregatedRankingPh1 <- getAggregatedRanking(dataDSCph1, rankingsPh1)
 
-# Remove individual subtask ranks, only keep mean
-rankingsMeanPh1[,2:27] <- NULL
-colnames(rankingsMeanPh1) <- c("Algorithm", "MeanRank")
+write.csv(aggregatedRankingPh1, "rankings-per-subtask/mean-ranking-ph1.csv")
 
-# Rank average rankings
-rankingsMeanPh1$rank <- rank(rankingsMeanPh1$MeanRank)
-write.csv(rankingsMeanPh1, "rankings-per-subtask/mean-ranking-ph1.csv")
 
 ###########################
 # Compute Phase 2 Results
 ###########################
 
 ## DSC metric results
 rankingsPh2_dsc <- computeSubtaskResults(dataDSCph2)
 write.csv(rankingsPh2_dsc, "rankings-per-subtask/subtask-rankings-ph2-dsc.csv")
 
 ## NSD metric results
 rankingsPh2_nsd <- computeSubtaskResults(dataNSDph2)
 write.csv(rankingsPh2_nsd, "rankings-per-subtask/subtask-rankings-ph2-nsd.csv")
 
-## Aggregate rankings by averaging over all subtasks
-rankingsMeanPh2 <- cbind(rankingsPh2_dsc, rankingsPh2_nsd[,2:5])
-
-# Calculate average ranks
-rankingsMeanPh2 <- cbind(rankingsMeanPh2,rowMeans(rankingsMeanPh2[,2:9]))
+## Aggregate rankings: Average per organ, then average over organ-scores
+# Merge DSC and NSD rankings in one data frame and rename columns
+rankingsPh2 <- cbind(rankingsPh2_dsc, rankingsPh2_nsd[,2:5])
+colnames(rankingsPh2)[2:5] <- paste(colnames(rankingsPh2)[2:5], "dsc", sep="_")
+colnames(rankingsPh2)[6:9] <- paste(colnames(rankingsPh2)[6:9], "nsd", sep="_")
 
-# Remove individual subtask ranks, only keep mean
-rankingsMeanPh2[,2:9] <- NULL
-colnames(rankingsMeanPh2) <- c("Algorithm", "MeanRank")
+# Get aggregated ranking
+aggregatedRankingPh2 <- getAggregatedRanking(dataDSCph2, rankingsPh2)
 
-# Rank average rankings
-rankingsMeanPh2$rank <- rank(rankingsMeanPh2$MeanRank)
-write.csv(rankingsMeanPh2, "rankings-per-subtask/mean-ranking-ph2.csv")
+write.csv(aggregatedRankingPh2, "rankings-per-subtask/mean-ranking-ph2.csv")
diff --git a/rankings-per-subtask/mean-ranking-ph1.csv b/rankings-per-subtask/mean-ranking-ph1.csv
index 82eece0..3fce5e2 100644
--- a/rankings-per-subtask/mean-ranking-ph1.csv
+++ b/rankings-per-subtask/mean-ranking-ph1.csv
@@ -1,20 +1,20 @@
-"","Algorithm","MeanRank","rank"
-"1","nnU-Net",1.34615384615385,1
-"2","K.A.V.athlon",2.96153846153846,2
-"3","NVDLMED",4.46153846153846,3
-"4","CerebriuDIKU",4.57692307692308,4
-"5","lupin",4.80769230769231,5
-"6","LS Wang's Group",5.30769230769231,6
-"7","MIMI",6.96153846153846,7
-"8","whale",8.88461538461539,8
-"9","BCVuniandes",9.19230769230769,9
-"10","VST",9.88461538461539,10
-"11","UBIlearn",10.3076923076923,11
-"12","BUT",10.5,12
-"13","A-REUMI01",10.6153846153846,13
-"14","LfB",11.8846153846154,14
-"15","jiafucang",13.3461538461538,15
-"16","AI-MED",14.4615384615385,16
-"17","Lesswire1",15.3461538461538,17
-"18","RegionTec",16.3846153846154,18
-"19","EdwardMa12593",16.7307692307692,19
+"","Algorithm","rank_Brain","rank_Heart","rank_Liver","rank_Hippocampus","rank_Prostate","rank_Lung","rank_Pancreas","MeanRank","rank"
+"1","nnU-Net",1,1.5,1,1,1,1,1,1.07142857142857,1
+"2","K.A.V.athlon",3,4,3,2,3.5,2,3,2.92857142857143,2
+"3","NVDLMED",4,1.5,2,8,6,8,2,4.5,3
+"4","CerebriuDIKU",2,10.5,7,3.5,5,3.5,5,5.21428571428571,5
+"5","lupin",7,3,4.5,3.5,3.5,6,6.5,4.85714285714286,4
+"6","LS Wang's Group",5,7.5,9,5,8.5,3.5,4,6.07142857142857,6
+"7","MIMI",8,12.5,8,6,7,5,9.5,8,7
+"8","whale",16,5,6,13,2,10,11,9,8
+"9","BCVuniandes",6,12.5,13.5,7,8.5,14.5,16,11.1428571428571,12
+"10","VST",12,7.5,11,9,11,10,9.5,10,9
+"11","UBIlearn",14,7.5,10,11.5,13,10,6.5,10.3571428571429,10
+"12","BUT",9,15,4.5,16,15,14.5,8,11.7142857142857,13
+"13","A-REUMI01",10,7.5,13.5,11.5,10,13,12,11.0714285714286,11
+"14","LfB",15,10.5,16,14,12,7,14,12.6428571428571,14
+"15","jiafucang",18,14,18,15,14,12,13,14.8571428571429,15
+"16","AI-MED",11,16,12,17,16,16.5,17.5,15.1428571428571,16
+"17","Lesswire1",13,18,15,18,18,16.5,17.5,16.5714285714286,17
+"18","RegionTec",17,18,17,19,19,19,15,17.7142857142857,19
+"19","EdwardMa12593",19,18,19,10,17,18,19,17.1428571428571,18
diff --git a/rankings-per-subtask/mean-ranking-ph2.csv b/rankings-per-subtask/mean-ranking-ph2.csv
index 3910618..e7f11c3 100644
--- a/rankings-per-subtask/mean-ranking-ph2.csv
+++ b/rankings-per-subtask/mean-ranking-ph2.csv
@@ -1,20 +1,20 @@
-"","Algorithm","MeanRank","rank"
-"1","nnU-Net",1,1
-"2","NVDLMED",1.625,2
-"3","K.A.V.athlon",2.25,3
-"4","LS Wang's Group",4.375,4
-"5","MIMI",6,5
-"6","CerebriuDIKU",6.5,6
-"7","whale",7.125,7
-"8","UBIlearn",7.875,8
-"9","lupin",9,9
-"10","jiafucang",9.75,10
-"11","A-REUMI01",10.25,11
-"12","LfB",10.375,12
-"13","VST",12.375,13
-"14","BUT",13.375,14
-"15","AI-MED",13.75,15
-"16","Lesswire1",14,16
-"17","RegionTec",15.5,17
-"18","BCVuniandes",16.375,18
-"19","EdwardMa12593",17.25,19
+"","Algorithm","rank_Hepatic Vessel","rank_Spleen","rank_Colon","MeanRank","rank"
+"1","nnU-Net",1,1,1.5,1.16666666666667,1
+"2","NVDLMED",2,3,1.5,2.16666666666667,2
+"3","K.A.V.athlon",3,2,3.5,2.83333333333333,3
+"4","LS Wang's Group",5.5,4,3.5,4.33333333333333,4
+"5","MIMI",4,7,6.5,5.83333333333333,5
+"6","CerebriuDIKU",7,8,5,6.66666666666667,6
+"7","whale",8,6,6.5,6.83333333333333,7
+"8","UBIlearn",9,5,12,8.66666666666667,8
+"9","lupin",5.5,10,16,10.5,10
+"10","jiafucang",11,9,11,10.3333333333333,9
+"11","A-REUMI01",10,16,8.5,11.5,12
+"12","LfB",12,12,8.5,10.8333333333333,11
+"13","VST",14,11,14,13,13
+"14","BUT",13,14.5,18.5,15.3333333333333,15.5
+"15","AI-MED",16,14.5,10,13.5,14
+"16","Lesswire1",15,18,13,15.3333333333333,15.5
+"17","RegionTec",18,13,16,15.6666666666667,17
+"18","BCVuniandes",17,17,18.5,17.5,18
+"19","EdwardMa12593",19,19,16,18,19