update readme

README.md

@@ -107,7 +107,7 @@ If you want to reduce memory consumption you can use the following options:
 * `--fast`: This will use a lower-resolution model
 * `--body_seg`: This will crop the image to the body region before processing it
 * `--roi_subset <list of classes>`: This will only predict a subset of classes
-* `--force_split`: This will split the image into 3 parts and process them one after another
+* `--force_split`: This will split the image into 3 parts and process them one after another. (Do not use this for small images. Splitting these into even smaller images will result in a field of view which is too small.)
 * `--nr_thr_saving 1`: Saving big images with several threads will take a lot of memory
 
 

totalsegmentator/bin/TotalSegmentator.py

@@ -84,7 +84,7 @@ def main():
                         help="Do initial rough body segmentation and crop image to body region",
                         default=False)
 
-    parser.add_argument("-fs", "--force_split", action="store_true", help="Process image in 3 chunks for less memory consumption",
+    parser.add_argument("-fs", "--force_split", action="store_true", help="Process image in 3 chunks for less memory consumption. (do not use on small images)",
                         default=False)
 
     parser.add_argument("-ss", "--skip_saving", action="store_true",

totalsegmentator/nnunet.py

@@ -321,6 +321,9 @@ def nnUNet_predict_image(file_in: Union[str, Path, Nifti1Image], file_out, task_
             dcm_to_nifti(file_in, tmp_dir / "dcm" / "converted_dcm.nii.gz", verbose=verbose)
             file_in_dcm = file_in
             file_in = tmp_dir / "dcm" / "converted_dcm.nii.gz"
+
+            # for debugging
+            # shutil.copy(file_in, file_in_dcm.parent / "converted_dcm_TMP.nii.gz")
 
             # Workaround to be able to access file_in on windows (see issue #106)
             # if platform.system() == "Windows":