README improvements

Author: CPBridge

README.md

@@ -83,7 +83,7 @@ python -m idc_annotation_conversion.pan_cancer_nuclei_seg --help
 
 The following modules are currently available:
 
-- `pan_cancer_nuclei_seg`: This module implements conversion of Conversion of Pan Cancer
+- `pan_cancer_nuclei_seg`: This module implements conversion of Pan Cancer
   Nuclei Segmentations for several collections within TCGA. The original data are supplied
   in a non-standard CSV format giving the image coordinates points on the contours of
   nuclei as segmented by a deep-learning based segmentation model. These data were previously released
@@ -94,14 +94,14 @@ The following modules are currently available:
   highest resolution, this process is very slow and memory intensive.
 
 - `rms`: Conversion of annotations related to the rhabdomyosarcoma mutation prediction project from the Frederick National Laboratory.
-  Both hand annotated regions (used as training data in the project) and model-generated prediction results are available.
+  Both hand annotated regions for tissue type (necrosis, stroma, ARMS, ERMS), used as training data in the project, and model-generated prediction results (for the same tissue classes) are available.
   Hand annotated regions are provided as ImageScope format XML annotations and are converted to DICOM Structured Report objects with the `convert-xml-annotations` sub-command.
-  Model-generated prediction results as probabilistic segmentation maps are provided as serialized NumPy arrays (`.npy` files) and converted to both binary and fractional DICOM Segmentation objects with the `convert-segmentations` sub-command.
+  Model-generated probabilistic segmentation maps are provided as serialized NumPy arrays (`.npy` files) and converted to both binary and fractional DICOM Segmentation objects with the `convert-segmentations` sub-command.
 
-- `tcga_til_maps`: There are two related collections here, both containing patch-level maps of tumor-infiltrating lymphocytes (TILs) predicted by a neural network for several collectsions within TCGA. The two collections correspond to two different versions of the model, published in 2018 and 2022 by the same lab at Stony Brook University. Their conversion routines are implemented as two separate sub-commands within this module.
+- `tcga_til_maps`: There are two versions of this collection, both containing patch-level maps of tumor-infiltrating lymphocytes (TILs) predicted by a neural network for several collections within TCGA. The two versions correspond to two different versions of the model, published in 2018 and 2022 by the same lab at Stony Brook University. Conversion routines for these two versions implemented as two separate sub-commands within this module.
 
-  The 2018 set covers a smaller subset of the TCGA collections. The algorithm is published in [this paper](https://www.cell.com/cell-reports/pdf/S2211-1247(18)30447-9.pdf) and the source files are available [here](https://stonybrookmedicine.app.box.com/v/cellreportspaper). The collection was also described by TCIA on [this page](https://www.cancerimagingarchive.net/analysis-result/til-wsi-tcga/). The files are supplied as low-resolution PNG images, where each pixel in the PNG corresponds to a 50 micron patch in the original slide and the pixel value indicates the presence of TILs within the patch. The `convert-2018` command converts these to binary DICOM segmentation objects.
+  The 2018 versions covers a smaller subset of the TCGA collections. The algorithm is published in [this paper](https://www.cell.com/cell-reports/pdf/S2211-1247(18)30447-9.pdf) and the source files are available [here](https://stonybrookmedicine.app.box.com/v/cellreportspaper). The collection was also described by TCIA on [this page](https://www.cancerimagingarchive.net/analysis-result/til-wsi-tcga/). The files are supplied as low-resolution PNG images, where each pixel in the PNG corresponds to a 50 micron patch in the original slide and the pixel value indicates the presence of TILs within the patch. The `convert-2018` command converts these to binary DICOM segmentation objects.
 
-  The 2022 set covers a wider range of TCGA images and additionally has probabilistic segmentations (before thresholding) available in addition to binarized versions. This algorithm is described in [this paper](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.806603/full) and the source data are available [here](https://stonybrookmedicine.box.com/v/til-results-new-model). These are supplied as a non-standard text file containing a list of patch coordinates and associated binary or probabilistic pixel values. The `convert-2022` command coverts these to pixel arrays and stores them as DICOM Segmentation objects, giving one binary and one fractional (probabilistic) segmentation object for each slide.
+  The 2022 versions covers a wider range of TCGA images and additionally has probabilistic segmentations (before thresholding) available in addition to binarized versions. This algorithm is described in [this paper](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.806603/full) and the source data are available [here](https://stonybrookmedicine.box.com/v/til-results-new-model). These are supplied as a non-standard text file containing a list of patch coordinates and associated binary or probabilistic pixel values. The `convert-2022` command coverts these to pixel arrays and stores them as DICOM Segmentation objects, giving one binary and one fractional (probabilistic) segmentation object for each slide.
 
-- `gbm_transcriptional_subtypes`: This modules relates to a collection of results from [this paper](https://www.nature.com/articles/s41467-023-39933-0) from Stanford University on transcriptional subtypes within glioblastoma. There are two data types of interest here: transcriptional subtype maps classifying an image patch into a set of transcriptional subtypes, and aggressiveness maps giving the aggressiveness of each image patch. While the conversion process for both, only the aggressiveness maps have been released at this time. The source data are not publicly available elsewhere. The aggressiveness maps are supplied as arrays of image coordinates and corresponding aggressiveness scores (between 0 and 1) within an h5 format file, with one aggressiveness score for an entire image patch. These are converted to DICOM Parametric Map objects using the `convert-aggressiveness-maps` sub-command of this module.
+- `gbm_transcriptional_subtypes`: This module relates to a collection of results from [this paper](https://www.nature.com/articles/s41467-023-39933-0) from Stanford University on transcriptional subtypes within glioblastoma. There are two data types of interest here: transcriptional subtype maps classifying an image patch into a set of transcriptional subtypes, and aggressiveness maps giving the aggressiveness of each image patch. While the conversion process for both is implemented in this repository, only the aggressiveness maps have been released at this time. The source data are not publicly available elsewhere. The aggressiveness maps are supplied as arrays of image coordinates and corresponding aggressiveness scores (between 0 and 1) within an h5 format file, with one aggressiveness score for an entire image patch. These are converted to DICOM Parametric Map objects using the `convert-aggressiveness-maps` sub-command of this module.