Update nvsegment-ct readme

NV-Segment-CT/docs/README.md

@@ -16,28 +16,88 @@ cd NV-Segment-CTMR/NV-Segment-CT;
 pip install -r requirements.txt;
 ```
 
-Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`;
+Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; later runs reuse the cached weights.
 
 ## 1.1 **NV-Segment-CT** [[Github]](https://github.com/NVIDIA-Medtech/NV-Segment-CTMR/tree/main/NV-Segment-CT) [[Huggingface]](https://huggingface.co/nvidia/NV-Segment-CT)
 
 ### Automatic Segmentation (support multi-gpu batch processing)
 
-[class definition](https://github.com/NVIDIA-Medtech/NV-Segment-CTMR/blob/main/NV-Segment-CTMR/configs/label_dict.json)
+[class definition](https://github.com/NVIDIA-Medtech/NV-Segment-CTMR/blob/main/NV-Segment-CT/configs/label_dict.json)
+
+#### Single image inference to segment everything (automatic)
+
+The output will be saved to `{output_dir}/spleen_03/spleen_03_{output_postfix}{output_ext}`.
 
 ```bash
-# CT sementation
+# Make sure conda environment is activated
+conda activate vista3d-nv
 cd NV-Segment-CT
+
 # Automatic Segment everything
 python -m monai.bundle run --config_file configs/inference.json --input_dict "{'image':'example/spleen_03.nii.gz'}"
+```
+
+#### Single image inference to segment specific class (automatic)
+
+The detailed automatic segmentation class index can be found [here](../configs/label_dict.json).
+
+```bash
 # Automatic Segment specific class
 python -m monai.bundle run --config_file configs/inference.json --input_dict "{'image':'example/spleen_03.nii.gz','label_prompt':[3]}"
-# Automatic Batch segmentation for the whole folder
+```
+
+#### Batch inference with multiGPU support (automatic)
+
+The `configs/batch_inference.json` defines the batch inference, you can:
+
+1. Segment all NIfTI files within a folder and subfolders
+
+    - `configs/batch_inference.json` builds `input_list` with `scripts/batch_inference_utils.build_input_list()`:
+    - Recursively discovers `**/*.nii.gz` under `--input_dir`.
+    - **Resume (default):** with `batch_resume_skip_existing: true` in `batch_inference.json`, only volumes whose expected output is **missing or empty** under `--output_dir` are queued (same layout as `SaveImaged`). Re-run the **same** command to finish leftovers. Set `batch_resume_skip_existing` to false to segment every discovered file again.
+    - **Discovery filters:** edit `batch_skip_dir_names` (exact parent folder name) and/or `batch_skip_dir_prefixes` (parent folder name starts with...) in `configs/batch_inference.json` (JSON arrays of strings).
+    - **Optional keys** in `configs/batch_inference.json` (defaults in the file): `batch_skip_dir_names`, `batch_skip_dir_prefixes`, `batch_resume_skip_existing`, `batch_use_input_list_cache`, `batch_cache_wait_sec`.
+    - **`batch_use_input_list_cache`:** With `torchrun` (multi-process), only rank 0 walks the tree to build `input_list` and writes a small JSON cache under the system temp directory; other ranks read that file so you do not repeat a huge filesystem scan on every GPU. Set to `false` if you want every rank to compute the list itself (simpler, slower on large cohorts). Single-process runs are unaffected in practice.
+    - **`batch_cache_wait_sec`:** When `batch_use_input_list_cache` is `true`, non-zero ranks wait up to this many seconds for rank 0's cache file. Increase if rank 0's scan is slow; decrease only if the list is always built quickly.
+    - **Which classes to segment:** edit **`everything_labels`** in **`configs/inference.json`**. See `configs/label_dict.json` and `docs/inference.md`.
+    - If **resume** leaves nothing to run (all outputs already present), the run **exits successfully** with `[nvseg] batch: nothing to run (resume); ok` (avoids a zero-length dataloader / `DistributedSampler` failure). If **no** `*.nii.gz` files are discovered under `input_dir`, you get a short `[nvseg] batch: no *.nii.gz...` error.
+    - Rank 0 logs: `[nvseg] batch resume (skip existing outputs): N volume(s) (...)`.
+    - **Multi-GPU:** `--nproc_per_node` must be less than or equal to the number of volumes in `input_list` after filtering.
+    - **Outputs:** With `data_root_dir` and `separate_folder: true`, `input_dir/patient1/scan.nii.gz` -> `output_dir/patient1/scan/scan_trans.nii.gz`. If `models/model.pt` is missing, inference prepares it automatically from Hugging Face.
+    - Advanced: edit `should_skip_path_by_parent_rules()` in `scripts/batch_inference_utils.py` for custom path rules.
+
+2. Segment based on a filelist.txt file, you can change the `input_list` in `configs/batch_inference.json`
+
+```json
+  "input_list": "$sorted([os.path.abspath(line.strip()) for line in open('/absolute/path/to/filelist.txt') if line.strip() and not line.strip().startswith('#')])",
+```
+
+##### Single-GPU Batch Inference
+
+```bash
+# Make sure conda environment is activated
+conda activate vista3d-nv
+cd NV-Segment-CT
+
 python -m monai.bundle run --config_file="['configs/inference.json', 'configs/batch_inference.json']" --input_dir="example/" --output_dir="example/"
-# Automatic Batch segmentation for the whole folder with multi-gpu support. mgpu_inference.json is below. change nproc_per_node to your GPU number.
-torchrun --nproc_per_node=2 --nnodes=1 -m monai.bundle run --config_file="['configs/inference.json', 'configs/batch_inference.json', 'configs/mgpu_inference.json']" --input_dir="example/" --output_dir="example/"
 ```
 
-Note: For more details about batch processing, please refer to NV-Segment-CTMR readme.md
+##### Multi-GPU batch inference (cohorts, resume, optional folder filters)
+
+```bash
+conda activate vista3d-nv
+cd NV-Segment-CT
+
+# Example: multi-GPU batch (same command for first run or resume)
+torchrun --nproc_per_node=2 --nnodes=1 -m monai.bundle run \
+  --config_file="['configs/inference.json', 'configs/batch_inference.json', 'configs/mgpu_inference.json']" \
+  --input_dir="example/" \
+  --output_dir="example/"
+```
+
+```text
+Note: if using a finetuned checkpoint whose label_mapping maps custom labels to global indexes "2, 20, 21", remove the `subclass` dict from inference.json since those values defined in `subclass` will trigger the wrong subclass segmentation.
+```
 
 ### Interactive segmentation
 

NV-Segment-CTMR/docs/README.md

@@ -33,7 +33,7 @@ cd NV-Segment-CTMR/NV-Segment-CTMR
 pip install -r requirements.txt
 ```
 
-Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`;
+Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; later runs reuse the cached weights.
 
 ## Automatic Segmentation (support multi-gpu batch processing)