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refactor(core): improve cylinder parameter snapping and optimization logic

Browse source 
Refactor the cylinder parameter selection and optimization process to
improve accuracy and robustness.

- Implement `snap_to_discrete_values_xfr` using a KDTree for efficient
  mapping of continuous diameter and length values to a predefined
  set of discrete points.
- Update `objective_function_xfr` to use the new snapping mechanism
  and introduce a weighted loss component for diameter and length.
- Adjust PSO optimization bounds and search ranges in `run_pso_torch_xfr`
  to better align with image dimensions and anatomical constraints.
- Refine scoring logic in `cl_score_torch_xfr` with updated penalty
  weights for overlaps and out-of-bone voxels.
- Update `config/constant.py` with new allowed diameter and length
  ranges.
- Improve `imaging/preprocessing.py` by making `PROGRESS_FILE` a
  parameter to allow per-output-directory progress tracking.
- Update `xfr_debug.py` with improved error handling and directory
  paths for batch processing.
This commit is contained in:
xfr 2026-08-26 22:36:07 +08:00
parent b76f0708f3
commit 0274e954be
13 changed files with 15357 additions and 65 deletions
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4
.gitignore vendored
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@ -213,4 +213,6 @@ marimo/_lsp/
__marimo__/
# Streamlit
.streamlit/secrets.toml
.streamlit/secrets.toml
progress.json

28
.kilo/agents/data.md Normal file
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@ -0,0 +1,28 @@
---
mode: primary
description: Run notebook-first data analysis by appending and executing cells
for each request.
options:
displayName: Data
id: data
requirements:
skills:
- data-investigation
vscode_extensions:
- name: Jupyter
id: ms-toolsai.jupyter
color: "#2563EB"
---
You are Kilo, a notebook-first data analysis agent. Use an active Jupyter notebook as the working surface.
Guidelines:
- If no notebook is active, create a uniquely named, descriptive `<topic>.ipynb` in the current workspace folder
- Use the dedicated notebook tools to create, read, edit, and execute; prefer these tools over other methods like MCP tools and manual raw JSON editing
- Confirm Jupyter and kernel readiness through the first requested notebook execution; only notify the user if they need to select or configure a kernel before work can continue
- For every user request, append at least one focused code cell and execute it
- Preserve notebook history: do not modify or delete existing cells unless explicitly asked; after failures, append diagnostic or corrected cells
- Keep substantive data work and supporting evidence in the notebook
- Avoid changing non-notebook files unless explicitly requested or necessary to complete the task
- Inspect cell output before answering, and keep notebook outputs and final summaries concise
- Never claim execution when a notebook cell did not run

4
.vscode/settings.json vendored
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@ -1,5 +1,7 @@
{
"python.analysis.extraPaths": [
"${workspaceFolder}"
]
],
"python-envs.defaultEnvManager": "ms-python.python:conda",
"python-envs.defaultPackageManager": "ms-python.python:conda"
}

16
config/constant.py
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@ -13,16 +13,26 @@ LABEL_MAP = {
20: "L1", 21: "L2", 22: "L3", 23: "L4", 24: "L5"
}
ALLOWED_DIAMETERS = [
3.5,
4.0,
# 3.5,
# 4.0,
4.5,
5.0,
5.5,
6.0,
6.5,
# 7.0,
# 7.5,
]
ALLOWED_LENGTHS = [
25,
30,
35,
40,
45,
50,
# 50,
# 60,
# 70,
# 80,
]
OVERLAP_THRESH = 0.50
DEFAULT_SPACING = [0.5, 0.5, 0.5]

51
core/cylinder.py
View file

@ -1,5 +1,9 @@
from scipy.spatial import KDTree
import torch
import numpy as np
from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
def create_coordinate_grid(
@ -38,7 +42,52 @@ def snap_to_discrete_values(diameter_raw, length_raw):
return diameter_discrete, length_discrete
def round_down_to_discrete_values(diameter_raw, length_raw):
points = (
(4.5, 25),
(4.5, 30),
(4.5, 35),
(5.0, 30),
(5.0, 35),
(5.0, 40),
(5.5, 30),
(5.5, 35),
(5.5, 40),
(5.5, 45),
(5.5, 50),
(6.0, 35),
(6.0, 40),
(6.0, 45),
(6.0, 50),
(6.0, 55),
(6.5, 35),
(6.5, 40),
(6.5, 45),
(6.5, 50),
(6.5, 55),
(7.0, 35),
(7.0, 40),
(7.0, 45),
(7.0, 50),
(7.5, 60),
(7.5, 70),
(7.5, 80),
)
tree = KDTree(points)
def snap_to_discrete_values_xfr(diameter_raw, length_raw):
return snap_to_discrete_values(diameter_raw, length_raw)
distance, index = tree.query((diameter_raw, length_raw))
return points[index][0], points[index][1]
def round_down_to_discrete_values_xfr(diameter_raw, length_raw):
"""
將連續值映射到小於等於該值的最接近允許離散值 (向下取整)

12
core/objective.py
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@ -5,7 +5,7 @@ from scipy.ndimage import map_coordinates
import numpy as np
import torch
from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, generate_cylinder_tip_torch
from core.cylinder import generate_cylinder_n_torch, generate_cylinder_o_torch, snap_to_discrete_values, generate_cylinder_tip_torch, snap_to_discrete_values_xfr
from core.intersection import center_line_intersections_torch
from core.scoring import cl_score_torch, cl_score_torch_xfr
@ -138,11 +138,14 @@ def objective_function_xfr(params: list[float], y_indices) -> float:
# 將連續值轉換為離散值
# diameter_discrete, length_discrete = snap_to_discrete_values(diameter_raw, length_raw)
diameter_discrete, length_discrete = diameter_raw, length_raw
diameter_discrete, length_discrete = snap_to_discrete_values_xfr(diameter_raw, length_raw)
diameter_loss = .9*diameter_discrete + .1*diameter_raw
length_loss = .9* length_discrete + .1* length_raw
loss = cylinder_circle_line_intersection_loss_deductions_torch(
diameter_discrete,
length_discrete,
diameter_loss,
length_loss,
position_params,
image2_shape,
cortical_tensor,
@ -164,7 +167,6 @@ def objective_function(params: list[float]) -> float:
# 將連續值轉換為離散值
diameter_discrete, length_discrete = snap_to_discrete_values(diameter_raw, length_raw)
# diameter_discrete, length_discrete = diameter_raw, length_raw
loss = cylinder_circle_line_intersection_loss_deductions_torch(
diameter_discrete,

51
core/optimizer.py
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@ -7,13 +7,15 @@ from config.constant import ALLOWED_DIAMETERS, ALLOWED_LENGTHS
from core.objective import objective_function, objective_function_xfr
from pyswarm import pso
import core.objective # <--- 加入這行，讓我們可以直接操作 objective 模組
from core.cylinder import generate_cylinder_n_torch, snap_to_discrete_values, create_coordinate_grid, round_down_to_discrete_values
from core.cylinder import generate_cylinder_n_torch, snap_to_discrete_values, create_coordinate_grid, snap_to_discrete_values_xfr
from core.scoring import compute_overlap_ratio_from_cylinder_mask, is_solution_ok
from config.constant import OVERLAP_THRESH
from visualization.res_plot_3d import res_plt_2_torch
def get_first_nonzero_y(arr):
OUTSIDE_VALUE = -100
# 1. Create a boolean mask where elements are non-zero
mask = arr != 0
@ -25,9 +27,10 @@ def get_first_nonzero_y(arr):
has_nonzero = np.any(mask, axis=1)
# 4. Replace indices where there were no non-zeros with a sentinel value (e.g., -1)
y_indices = np.where(has_nonzero, y_indices, -1)
y_indices = np.where(has_nonzero, y_indices, OUTSIDE_VALUE)
y_indices = np.where(y_indices > arr.shape[1] * .4, -1, y_indices)
y_indices = np.where(y_indices < arr.shape[1] * .1, OUTSIDE_VALUE, y_indices)
y_indices = np.where(y_indices > arr.shape[1] * .4, OUTSIDE_VALUE, y_indices)
return y_indices.astype(np.float32)
@ -115,13 +118,16 @@ def run_pso_torch_xfr(
# flat_min_index = np.argmin(y_indices)
# z_border, x_border = np.unravel_index(flat_min_index, y_indices.shape)
x_with_nonzero = np.where(np.any(image2_array[:,9,:] != 0, axis=0))[0]
x_with_nonzero = np.where(np.any(image2_array[:,image_shape[1]//10,:] != 0, axis=0))[0]
x1 = x_with_nonzero[0]
x2 = x_with_nonzero[-1]
# print(x1,x2)
# exit()
x_mid = (x1+x2)/2
x1 = x_mid-9
x2 = x_mid+9
x1 = x_mid-image_shape[2]*.1
x2 = x_mid+image_shape[2]*.1
z_sum = np.sum(image2_array, axis=(1, 2))
z_with_nonzero = np.where(z_sum > 0)[0]
@ -141,19 +147,20 @@ def run_pso_torch_xfr(
# 設定基本的 bounds
if CBT == True:
z_bounds = (0, image_shape[0] / 2)
# z_bounds = (0, image_shape[0]-1)
# z_bounds = (z1, (z1+z2)/2)
x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
z_bounds = (.1*image_shape[0], .8*image_shape[0])
# x_bounds_right = (image_shape[2]/2 + image_shape[2]/10, image_shape[2] - 1)
# x_bounds_left = (0, image_shape[2]/2 - image_shape[2]/10 - 1)
x_bounds_right = (x2, image_shape[2]*.9)
x_bounds_left = (image_shape[2]*.1, x1)
# azimuth_bounds_l = ((95-azi), (145-azi))
# azimuth_bounds_r = ((50-azi), (85-azi))
# altitude_bounds = ((60-alt), (75-alt))
azimuth_bounds_l = ((95-azi), (105-azi))
azimuth_bounds_r = ((75-azi), (85-azi))
altitude_bounds = ((55-alt), (70-alt))
azimuth_bounds_l = ((98-azi), (120-azi))
azimuth_bounds_r = ((60-azi), (82-azi))
altitude_bounds = ((60-alt), (70-alt))
else:
z_bounds = (0, image_shape[0] - 1)
@ -173,7 +180,7 @@ def run_pso_torch_xfr(
"""
if optimize_size:
# d, L = snap_to_discrete_values(pos[5], pos[6])
d, L = round_down_to_discrete_values(pos[5], pos[6])
d, L = snap_to_discrete_values_xfr(pos[5], pos[6])
params_5 = pos[:5]
else:
d, L = diameter, length
@ -221,7 +228,7 @@ def run_pso_torch_xfr(
lb_r = [z_bounds[0], y_bounds[0], x_bounds_right[0], azimuth_bounds_r[0], altitude_bounds[0]]
ub_r = [z_bounds[1], y_bounds[1], x_bounds_right[1], azimuth_bounds_r[1], altitude_bounds[1]]
if debug:
if True or debug:
print(lb_l)
print(ub_l)
print(lb_r)
@ -233,7 +240,7 @@ def run_pso_torch_xfr(
best_position_r = None
# Left side optimization
print("\n=== 左側 ===")
print(f"\n=== {label_str} 左側 ===")
kwargs = {'y_indices': y_indices}
position_l, loss_l = pso(objective_function_xfr, lb_l, ub_l,
@ -299,7 +306,7 @@ def run_pso_torch_xfr(
# retries += 1
# Right side optimization
print("\n=== 右側 ===")
print(f"\n=== {label_str} 右側 ===")
position_r, loss_r = pso(objective_function_xfr, lb_r, ub_r,
# ieqcons=[constraint_y],
kwargs=kwargs,
@ -318,7 +325,7 @@ def run_pso_torch_xfr(
if optimize_size:
# diameter_r, length_r = snap_to_discrete_values(position_r[5], position_r[6])
diameter_r, length_r = round_down_to_discrete_values(position_r[5], position_r[6])
diameter_r, length_r = snap_to_discrete_values_xfr(position_r[5], position_r[6])
print(f"[RIGHT] Position: {position_r[:5]}")
print(f"[RIGHT] Diameter: {diameter_r} mm (raw: {position_r[5]:.2f})")
print(f"[RIGHT] Length: {length_r} mm (raw: {position_r[6]:.2f})")
@ -376,7 +383,7 @@ def run_pso_torch_xfr(
final_diameter_r = best_position_r[5]
final_length_r = best_position_r[6]
print(f"\n=== 最終結果 ===")
print(f"\n=== {label_str} 最終結果 ===")
print(f"Left - Diameter: {final_diameter_l} mm, Length: {final_length_l} mm")
print(f"Right - Diameter: {final_diameter_r} mm, Length: {final_length_r} mm")
else:
@ -520,7 +527,7 @@ def run_pso_torch(
"""
if optimize_size:
# d, L = snap_to_discrete_values(pos[5], pos[6])
d, L = round_down_to_discrete_values(pos[5], pos[6])
d, L = snap_to_discrete_values_xfr(pos[5], pos[6])
params_5 = pos[:5]
else:
d, L = diameter, length
@ -643,7 +650,7 @@ def run_pso_torch(
if optimize_size:
# diameter_r, length_r = snap_to_discrete_values(position_r[5], position_r[6])
diameter_r, length_r = round_down_to_discrete_values(position_r[5], position_r[6])
diameter_r, length_r = snap_to_discrete_values_xfr(position_r[5], position_r[6])
print(f"[RIGHT] Position: {position_r[:5]}")
print(f"[RIGHT] Diameter: {diameter_r} mm (raw: {position_r[5]:.2f})")
print(f"[RIGHT] Length: {length_r} mm (raw: {position_r[6]:.2f})")
@ -836,7 +843,7 @@ def run_de_torch(
def eval_overlap_from_position(pos, side: str, optimize_size: bool, spine_tensor: torch.Tensor, image_shape, spacing):
if optimize_size:
# d, L = snap_to_discrete_values(pos[5], pos[6])
d, L = round_down_to_discrete_values(pos[5], pos[6])
d, L = snap_to_discrete_values_xfr(pos[5], pos[6])
params_5 = pos[:5]
else:
d, L = diameter, length
@ -1015,7 +1022,7 @@ def run_nm_torch(
def eval_overlap_from_position(pos, side: str, optimize_size: bool, spine_tensor: torch.Tensor, image_shape, spacing):
if optimize_size:
# d, L = snap_to_discrete_values(pos[5], pos[6])
d, L = round_down_to_discrete_values(pos[5], pos[6])
d, L = snap_to_discrete_values_xfr(pos[5], pos[6])
params_5 = pos[:5]
else:
d, L = diameter, length

13
core/scoring.py
View file

@ -23,9 +23,8 @@ def cl_score_torch_xfr(
in_bone= ((spine_tensor == 1) & (cylinder_torch == 1)).sum().item()
not_in_bone= ((spine_tensor == 0) & (cylinder_torch == 1)).sum().item()
# if cyl_total == 0:
# return float(1000*1000)
# return float(1e9) # 極差的情況
if cyl_total == 0:
return float(1e9) # 極差的情況
overlap_ratio = overlap / cyl_total
@ -37,10 +36,10 @@ def cl_score_torch_xfr(
# if in_bone == 0:
# return float(not_in_bone*200)
score += overlap*30
score += in_bone*10
score -= not_in_bone*1000
score -= null_vox2*1000
score += 10 * in_bone # 10 實在太低
score += 100 * overlap
score -= 2000 * max(0, not_in_bone-10)
score -= 1000 * max(0, null_vox2-10)
return float(-score)

12
imaging/preprocessing.py
View file

@ -8,15 +8,15 @@ import glob
from config.constant import LABEL_MAP
from imaging.nifti_io import sitk_to_nibabel, nibabel_to_sitk
PROGRESS_FILE = "progress.json"
def load_progress():
def load_progress(PROGRESS_FILE):
if os.path.exists(PROGRESS_FILE):
with open(PROGRESS_FILE, "r") as f:
return json.load(f)
return {}
def save_progress(progress):
def save_progress(progress, PROGRESS_FILE):
with open(PROGRESS_FILE, "w") as f:
json.dump(progress, f, indent=2)
@ -73,7 +73,9 @@ def process_dataset(image_dir, label_dir, output_dir, labels_to_process=None):
total_files = len(image_files)
print(f"Total files: {total_files}")
progress = load_progress()
PROGRESS_FILE = os.path.join(output_dir, "progress.json")
progress = load_progress(PROGRESS_FILE)
all_file_summary = []
for idx, image_path in enumerate(image_files, 1):
@ -119,7 +121,7 @@ def process_dataset(image_dir, label_dir, output_dir, labels_to_process=None):
"processed_labels": result["processed_labels"],
"missing_labels": result["missing_labels"]
}
save_progress(progress)
save_progress(progress, PROGRESS_FILE)
print(f"[{idx}/{total_files}] Finished: {file_name} | Missing labels: {result['missing_labels'] or 'None'}")

2
imaging/segmentation.py
View file

@ -35,7 +35,7 @@ def seg_bone(n, name, resampled_sitk_img, resampled_sitk_lbl, output_base=None,
# 2. 獲取所有連通區域
# 連通區域濾波器會將 binary_mask 中的不同物體標記為 1, 2, 3...
cc_image = sitk.ConnectedComponent(binary_mask)
cc_image = sitk.ConnectedComponent(binary_mask, True) #fullyConnected
# 3. 根據區域大小（像素/體積）重新標記
# RelabelComponent 會按大小排序，最大的物體標籤會被設為 1

15175
progress.json
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File diff suppressed because it is too large Load diff

52
xfr_debug.py
View file

@ -9,10 +9,10 @@ from core.objective import set_global_context
from core.optimizer import run_pso_torch, run_de_torch, run_nm_torch, run_pso_torch_xfr
from imaging.orientation import azimuth_rotation, analyze_vertebral_tilt_contour
standardized_dir = '/mnt/1248/open/cyrou/CBT/Seg/Resample/standardized-xfr/'
standardized_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr/'
azimuth_rotation_dir = '/mnt/1248/open/cyrou/azimuth_rotation'
tilt_contour_dir = '/mnt/1248/open/cyrou/tilt_contour'
azimuth_rotation_dir = '/mnt/1248/open2/cyrou/azimuth_rotation'
tilt_contour_dir = '/mnt/1248/open2/cyrou/tilt_contour'
Output_dir = '/mnt/1248/open/cyrou/Output'
def get_device(gpu_id=None):
@ -95,7 +95,7 @@ def debug_pso(volume_id, level):
)
best_l, loss_l, best_r, loss_r, total_time = run_pso_torch_xfr(
label_str=level,
label_str=f'{volume_id} {level}',
image1_path=cortical_path,
image2_path=binary_path,
image3_path=roi_path,
@ -121,21 +121,37 @@ def main():
# process_volume(volume_id, level)
# exit()
for volume_id in (
'1.3.6.1.4.1.9328.50.4.0001',
# '1.3.6.1.4.1.9328.50.4.0002',
# '1.3.6.1.4.1.9328.50.4.0003',
# '1.3.6.1.4.1.9328.50.4.0004',
# '1.3.6.1.4.1.9328.50.4.0005',
# '1.3.6.1.4.1.9328.50.4.0006',
):
# for volume_id in sorted(os.listdir(standardized_dir)):
# debug_orientation(volume_id, level)
for level in ('L1', 'L2', 'L3', 'L4', 'L5'):
# debug_orientation(volume_id, level)
debug_pso(volume_id, level)
MAX_SUCCESSFUL_VOLUMES = 100
number_of_successful_volumes = 0
exit()
# for volume_id in (
# '1.3.6.1.4.1.9328.50.4.0001',
# '1.3.6.1.4.1.9328.50.4.0002',
# '1.3.6.1.4.1.9328.50.4.0003',
# '1.3.6.1.4.1.9328.50.4.0004',
# '1.3.6.1.4.1.9328.50.4.0005',
# # '1.3.6.1.4.1.9328.50.4.0006',
# ):
for volume_id in sorted(os.listdir(standardized_dir)):
# debug_orientation(volume_id, level)
error_flag = False
for level in ('L1', 'L2', 'L3', 'L4', 'L5'):
# for level in ('L5',):
# debug_orientation(volume_id, level)
try:
debug_pso(volume_id, level)
except Exception as e:
print(f'Error in {volume_id} {level}: {e}')
error_flag = True
continue
if not error_flag:
number_of_successful_volumes += 1
if number_of_successful_volumes >= MAX_SUCCESSFUL_VOLUMES:
break
# exit()

2
xfr_preprocess.py
View file

@ -4,7 +4,7 @@ from imaging.preprocessing import process_dataset
data_root = '/mnt/1220/Public/dataset/Spine/CTSpine1K/data/'
label_root = '/mnt/1220/Public/dataset/Spine/CTSpine1K/label/'
output_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr/'
output_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr-2/'
label_map = {
'colon': 'conlon',